University of Malaya Kuala Lumpur

بسم هللا الرحمن الرحيم ريقيAالهم اسئلك باسمك النور ان تنور لي ط FEASIBILITY ANALYSIS ON THE POTENTIAL OF PHOTOVOLTAIC APPLICATIONS IN PENINSULAR MALAYSIA

سن ي ح ا ل, سن حا ل ,, علي, ا اله , ص , طه,ا ل اقن ل كريم

ALI WADI ABBAS AL-FATLAWI

Malaya

THESIS SUBMITTED IN FULFILLMENTof OF THE REQUIREMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

FACULTY OF ENGINEERING UniversityUNIVERSITY OF MALAYA KUALA LUMPUR

2015

UNIVERSITY MALAYA

ORIGINAL LITERARY WORK DECLARATION

Name of the candidate: Ali Wadi Abbas Al-Fatlawi

Registration/Matric No: KHA100035

Name of the Degree: Doctor of Philosophy

Title of Project Paper/ Research Report/ Dissertation / Thesis (“this work”): Feasibility Analysis on the Potential of Photovoltaic Applications in Peninsular Malaysia.

Field of Study: Energy

I do solemnly and sincerely declare that:

(1) I am the sole author /writer of this work; (2) This work is original; Malaya (3) Any use of any work in which copyright exists was done by way of fair dealings and any expert or extract from, or reference to or reproduction of any copyright work has been disclosed expressly and sufficiently and the title of the Work, and its authorship has been acknowledged in this Work; of

(4) I do not have any actual knowledge nor do I ought reasonably to know that the making of this work constitutes an infringement of any copyright work;

(5) I hereby assign all and every rights in the copyrights to this Work to the University of Malaya ( UM), who henceforth shall be owner of the copyright in this Work and that any reproduction or use in any form or by any means whatsoever is prohibited without the written consent of UM having been first had and obtained actual knowledge;

(6) I am fully aware that if in the course of making this Work I have infringed any copyright whether internationally or otherwise, I may be subject to legal action or any other action as may be determined by UM. Candidate’sUniversity Signature Date: Subscribed and solemnly declared before,

Witness’s Signature Date:

Name:

Designation:

ii ABSTRACT

This study was conducted to enhance the utilization of renewable energy resources to contribute towards national electricity supply sustainability and sustainable socio-economic development. Four methodologies were developed starting with solar radiation modeling using artificial neural networks (ANNs), followed by modeling and investigating the feasibility of grid-connected solar PV applications under the Feed-in-Tariff (FIT) program. After that, a sensitivity analysis was developed based on economic indicators. Next, a solar PV power hybrid standalone street light application was modeled for remote areas where electricity extension can be made cost-effective. Lastly, people's acceptance, and obstacles to using solar energy applications were investigated. The outcomes of the resource analyses show that the ANN solar maps are sufficiently accurate to be utilizedMalaya with solar radiation applications in Peninsular Malaysia, whereby from all 12 designed models, the mean absolute percentage error (MAPE) was less than 6.07%. The model withof a MAPE value of 4.04% and correlation (R) of 0.965 attained a fairly steady reading, which can be safely inferred as a high reliability level in terms of weather data prediction. Solar radiation mapping indicates the annual radiation in

Peninsular Malaysia is between 3.88 and 5.13 kWh/m2/day. However, the northeast region has the highest potential for solar energy throughout the year. In this context, the renewable energy outputs from the grid-connected solar PV in pilot locations varied between a maximum of

1.312 MWh/kWp in Lubok Merbau and a minimum of 1.010 MWh/kWp in Cameron Highlands,University with capacity factors of 15% and 11.5% respectively. The cost of electricity ranges from $0.36/kWh for the most favorable locations to $0.28/kWh for less favorable locations, while the mean cost in the entire peninsular region is about $0.31/kWh. Government incentives are expected to reduce the payback period by approximately 73%, with the current payback periods ranging from a minimum of 7.4 years in Beyan Lepas to a maximum of 9.6 years in

iii

Cameron Highlands, while the mean payback period in Peninsular Malaysia is expected to be 8 years. According to a street light application analysis, using renewable energy-based solar radiation for street lighting is not recommended in Malaysia mostly due to the frequent occurrence of continuously heavy cloud cover and rainy days. Such conditions require a proportionate increase in PV size as well as number of batteries in order to adequately harvest the required energy in a shorter time span, hence causing an overall increase in the cost of providing street light poles. Based on the social acceptance of solar energy technology, the survey shows significant potential to implement solar PV applications in Malaysia.

Nonetheless, there has also been considerable failure to provide promotional informative features of solar PV applications, where people are still not even familiar with the term Feed- in-Tariff. However, around 36% are potentially ready to begin PV projects but require loans to reduce the starting capital costs, even if the loans stipulate sharing the FIT benefits with banks in advance.

Keywords Solar Energy in Malaysia; Solar resources Assessment; Artificial neural network;

Solar Applications; Modeling PV applications; Social Acceptance.

University of Malaya

iv ABSTRAK

Kajian ini adalah satu kajian kontemporari dibangunkan untuk meningkatkan penggunaan sumber tenaga asli yang boleh diperbaharui untuk menyumbang ke arah negara kemampanan bekalan elektrik dan pembangunan sosio-ekonomi yang mampan. Empat kaedah dibangunkan bermula dengan pemodelan sinaran suria menggunakan rangkaian neural ANN. Kemudian permodelan dan penyiasatan kemungkinan aplikasi PV solar yang diambung dengan grid di bawah program “Feed-in-Tariff” untuk membangunkan satu analisis sensitiviti berdasarkan petunjuk ekonomi. Kemudian model solar kuasa PV hibrid bersendirian untuk permohonan lampu jalan di kawasan-kawasan pedalaman di mana sambungan elektrik boleh dibuat pada kos yang efektif. Akhir sekali, menyiasat penerimaan dan halangan ke atas pengunaan aplikasi solar-tenaga rakyat. Hasil analisis sumber ini menunjukkanMalaya bahawa peta solar menggunakan ANN adalah cukup tepat untuk aplikasi radiasi solar di Semenanjung Malaysia, di mana 12 daripada satu model yang direka, iaitu of MAPE adalah kurang daripada 6.07%. Walau bagaimanapun, model yang mempunyai nilai MAPE daripada 4.04% dan korelasi (R) 0.965 telah mencapai bacaan agak stabil yang boleh diambil dengan selamat dan disimpulkan sebagai tahap kebolehpercayaan yang tinggi dari segi ramalan data cuaca. Pemetaan sinaran suria ini menunjukkan sinaran tahunan di Semenanjung Malaysia antara 3.88 dan 5.13 kWh/m2/day dan rantau timur laut mempunyai potensi solar tenaga yang paling tinggi sepanjang tahun.

Hasil tenaga boleh diperbaharui daripada PV grid yang berkaitan di lokasi perintis diubah antaraUniversity maksimum 1.312 MWh / kWp di Lubok Merbau dan sekurang-kurangnya 1.010 MWh / kWp di Cameron Highlands, dengan faktor-faktor keupayaan masing-masing sebanyak 15% dan 11.5%. Kos elektrik untuk lokasi yang paling baik adalah $ 0.36/kWh dan $ 0.28/kWh untuk lokasi yang kurang, manakala harga purata di seluruh Semenanjung adalah kira-kira $

0.31/kWh. Insentif kerajaan dijangka akan mengurangkan tempoh bayar balik kira-kira 73%

v tempoh bayar balik semasa terdiri daripada nilai minimum sebanyak 7.4 tahun di Bayan Lepas kepada nilai maksimum sebanyak 9.6 tahun di Cameron Highlands, manakala tempoh bayar balik minimum di Semenanjung Malaysia adalah dijangka 8 tahun. Analisis cahaya permohonan jalan menunjukkan bahawa menggunakan sinaran-tenaga boleh diperbaharui berasaskan solar untuk lampu kuasa jalanan tidak digalakkan di Malaysia kerana banyak kejadian awan terus berat dan hari hujan yang kerap berlaku dan memerlukan satu peningkatan berkadar dalam saiz PV serta bilangan bateri untuk secukupnya untuk menuai tenaga yang diperlukan dalam jangka masa yang lebih pendek. Dengan itu, ini menyebabkan peningkatan keseluruhan dalam kos menyediakan tiang lampu jalan. Berdasarkan penerimaan teknologi tenaga solar di kalangan rakyat Malaysia, kaji selidik menunjukkan bahawa potensi yang besar dalam penggunaan aplikasi PV solar di Malaysia. Malaya Tetapi ada juga kegagalan yang ketara kepada ciri-ciri promosi informatif aplikasi PV solar di mana orang yang masih tidak tahu jangka “Feed-in-Tariff”. Walau bagaimanapun,of terdapat kira-kira 36% daripada orang-orang yang bersedia untuk berkongsi dengan bank untuk mendapatkan manfaat daripada program

FiT. Perkara ini perlu tekun dilihat dengan serius oleh bank-bank dengan mengunakkan kajian daya maju mereka.

University

ACKNOWLEDGEMENT

Before everything else I do, I like to thank ALLAH the greatest creator for all the blessings He has granted me in the form physical health, sane mind willing to study hard and the conducive environmental situations He has put me in surrounded by lots of good friends who have always given me the encouragement to jointly study together and to share and solve our problems together. He has also given me the tenacity and determination to face all the challenges during my work. I also hereby send my salam to Prophet Muhammad and Ahl Al-Bayt, who are our exemplary embodiments of good deed performers through history teaching us how we should inwardly feel when our fellow brothers are in trouble to solve deadly problems that face the world of today. Malaya I would like to thank all my family members, especially, my mother and my father, my wife, my brothers and my sisters who never fail toof give me that much needed moral support and their continuous love without which I would not have made my study here in Malaysia.

I would like to express my deep gratitude and profound respect to Prof. Nasrudin Abd. Rahim and Prof. Saidur Rahman, for their acceptance to be my supervisors in this project and for all that I have learned from them during the research. Their continuous help and support in all stages of this thesis are immensely priceless.

My deep appreciation goes to my closest friends, Hossain Tugan, MD. Shouquat Hossain, FayazUniversity Hussain, Atika and Ali Ajam on how they have always made my environmental situations becoming friendly and for their unselfish help during the thesis writing and practical site surveys that had been successfully conducted in Malaysia, and my sincere thanks are also directed to the ever supportive Maryam in the geography department for providing me with the much needed software programs that I used for the first objective in this research.

vii

I acknowledge with deep gratitude the intangible contributions from all those people who support me even with a nice word of advice or encouragement that goes a long way to make me feel confident of what I do and shall perform for the benefit of mankind from the knowledge that I have acquired.

May Allah shower His blessings on them in the form of health, safety, purity of deeds, welfare and prosperity. Amin

Malaya of

University

viii

TABLE OF CONTENTS

UNIVERSITY MALAYA...... ii

ORIGINAL LITERARY WORK DECLARATION ...... ii

ABSTRACT ...... iii

ACKNOWLEDGEMENT ...... vii

TABLE OF CONTENTS...... ix

LIST OF FIGURES ...... xiii

LIST OF TABELS ...... xvii

LIST OF SYMBOLS AND ABBREVIATIONS ...... xix

CHAPTER 1: INTRODUCTION ...... 1 1.1 Background ...... Malaya...... 1 1.2 Global Environmental Concerns ...... 3 1.3 Advantages of Solar Energy ...... of 5 1.4 Problem Statement ...... 7

1.5 Thesis Objectives ...... 8

1.6 Scope of the Study ...... 9

1.7 Significance of the Study ...... 10

1.8 Thesis Outline ...... 11

CHAPTER 2: LITERATURE REVIEW ...... 14 2.1 UniversityIntroduction ...... 14 2.2 Review of Solar Resource Assessment Technique ...... 15

2.2.1 Solar Radiation Prediction using ANN ...... 19

2.2.2 Solar Radiation Mapping ...... 24

2.3 Review of Grid-Connected Solar PV Applications ...... 26

2.4 Review of Renewable Energy Policies in Malaysia ...... 28

2.4.1 Incentives and Fiscal Support ...... 29

2.4.2 Renewable Energy Programs ...... 34

2.5 Review of Current PV Technology for Power Supply ...... 35

2.5.1 Clean Energy Cost Criteria ...... 38

2.5.2 Clean Energy Evaluation Tool ...... 41

2.6 Social Acceptance Review ...... 44

2.7 Summary ...... 46

CHAPTER 3: METHODOLOGY ...... 48

3.1 Introduction ...... 48 3.2 Modeling Solar Radiation Using Artificial Neural MalayaNetworking ...... 49 3.2.1 Location & Data Utilized ...... 49 3.2.2 Modelling Solar Radiation ...... of ...... 51 3.2.3 Artificial Neural Networking (ANN) ...... 52

3.2.4 Design of the Artificial Neural Networks Model ...... 55

3.2.5 Accuracy of Solar Radiation Predictions ...... 57

3.2.6 Mapping Solar Radiation ...... 57

3.3 Modeling Solar PV of Grid-Connected Applications ...... 58

3.3.1 Photovoltaic System Modelling ...... 59 University3.3.2 Solar Radiation Modelling ...... 62 3.3.2 (a) Extraterrestrial Radiation ...... 63

3.3.2 (b) Clarity Index ...... 64

3.3.2 (c) Direct and Diffuse Solar Radiation ...... 64

3.3.3 Photovoltaic Model Assumptions ...... 65

3.3.4 PV System Cost Analysis ...... 66

3.3.5 Cost Analysis Assumptions ...... 70

3.4 Modeling Solar PV of Street Light Applications ...... 71

3.4.1 SPSL Model Description ...... 72

3.4.1(a) Battery and Controller ...... 73

3.4.1(b) Photovoltaic (PV) Generator ...... 74

3.4.1(c) Wind Turbine Generator ...... 74

3.4.2 SPSL Model Assumptions ...... 75

3.5 Acceptability of Renewable Energy Policy ...... 76

CHAPTER 4: RESULTS AND DISCUSSION ...... 77 4.1 Introduction ...... Malaya...... 77 4.2 Assessment of Solar Radiation ...... 77 4.2.1 Solar Radiation Prediction ...... of ...... 78 4.2.2 Solar Radiation Mapping ...... 81

4.3 Feasibility of PV electricity in Peninsular Malaysia...... 85

4.3.1 Solar Radiation Intensity in Peninsular Malaysia ...... 85

4.3.2 PV Electricity Generated in Peninsular Malaysia ...... 86

4.3.3 Economic Indicators Utilizing PV System ...... 87

4.3.4 Potential CO2 Abatement in Peninsular Malaysia ...... 91 4.4 UniversitySensitivity Analysis of PV Parameters ...... 91 4.4.1 Solar Resources in Kuala Lumpur ...... 91

4.4.2 Performance of Grid Connected Solar PV ...... 92

4.4.3 Impact of Wind Speed on PV Energy ...... 93

4.4.4 Impact of Increasing System Capacity ...... 95

4.4.5 Impact of Ground reflectance ...... 97

4.4.6 Impact of Temperature Coefficient ...... 97

4.4.7 Impact of PV slope and azimuth angles ...... 98

4.4.8 Impact of Real interest rate ...... 99

4.4.9 Impact of Hypothetical Electric Rate Increase ...... 99

4.4.10 Experimental work and Validation Results in KL ...... 100

4.5 Assessment of Street Light Applications ...... 104

4.6 Acceptance Solar Energy Application ...... 108

CHAPTER 5: CONCLUSIONS ...... 116

5.1 Introduction ...... 116 5.2 Solar Radiation Assessment Using ANN ...... Malaya...... 116 5.3 Grid-Connected PV system Assessment ...... 117 5.4 Sensitivity Analysis of PV parameters...... of ...... 118 5.5 Standalone Street Light Assessment ...... 119

5.6 Social Acceptance of Renewable Energy ...... 120

5.7 Recommendation for future work ...... 123

APPENDICES ...... 124

Appendix A: Predicted Solar Radiation ...... 124

Appendix B: Solar Radiation Mapping ...... 196 AppendixUniversity C: Grid Connected System ...... 208 Appendix D: Questionnaire and Survey Data ...... 211

Part A: Questionnaire questions ...... 211

Part B: Questionnaire feedback...... 214

REFERENCE ...... 217

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LIST OF FIGURES

Figure 1.1 Primary energy supply by fuel type in Malaysia ...... 2

Figure 1.2 Final energy consumption by sector in Malaysia ...... 2

Figure 1.3 Carbon dioxide emissions resulting from the consumption of oil, natural gas and coal Source: data resource ...... 3

Figure 2.1 Pipeline for solar radiation prediction through ANN ...... 20

Figure 2.2 Renewable energy target in the 10th Malaysia Plan ...... 30

Figure 2.3 Global marketing of different PV technologies ...... 37

Figure 2.4 The progressive development of solar PV efficiency ...... 38

Figure 2.5 Summary of the average LCOE values for Malayadifferent technologies used in power plants entering service in 2016 in the US ...... of ...... 40 Figure 2.6 Principal layered tasks of the HOMER program ...... 42

Figure 3.1 Geographical locations of the meteorological stations in Peninsular Malaysia...... 50

Figure 3.2 ANN interconnected groups of nodes, similar to the vast network of neurons in a human brain. Here, each circular node represents an artificial cell and the arrows represent the interior connections between cells...... 54

Figure 3.3 ANN dependency graph, including the input, summation function, transfer function andUniversity output...... 54 Figure 3.4 Feedback error that adjusts the weight between neurons...... 55

Figure 3.5 Typical neural network for global radiation prediction...... 56

Figure 3.6 Grid-connected PV system and its components (inside energy analysis room) ...... 61

Figure 3.7 Distribution of PV capital cost ...... 68

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Figure 4.1 Correlation coefficient (R) between predicted and measured solar radiation during the training process of Model#3, where read line is the measured data and blue line best fit for predicted data...... 79

Figure 4.2 Comparison between solar radiation predicted and measured for Petaling Jaya station...... 80

Figure 4.3 Comparison between: (a) the annual global solar radiation incident on the ground ,

(b) the complex terrain of Peninsular Malaysia ...... 83

Figure 4.4 Solar radiation maps for the monthly mean of daily solar insolation (kWh/m2/day) incident on a horizontal surface in peninsular Malaysia for (a) January, (b) April, (c) July, and

(d) Octobar ...... 84 Figure 4.5 Seasonal variations of solar radiation and clearnessMalaya index in Malaysia...... 85 Figure 4.6 Seasonal variations of solar radiation and clearness index...... 86 Figure 4.7 Average energy production each yearof and capacity factor...... 87 Figure 4.8 Comparison of energy cost levels and capacity factor at selected sites...... 88

Figure 4.9 Comparison of payback period of selected sites...... 89

Figure 4.10 Solar radiation absorption due to the atmosphere effect in Kuala Lumpur ...... 92

Figure 4.11 Weather temperature and PV cell temperature, with and without wind speed for the development of models through three continuous days...... 94

Figure 4.12 (A) Expected LCOE for different sizes of solar PV system...... 95 FigureUniversity 4.12 (B) Expected Payback period for different sizes of solar PV system……………..96 Figure 4.13 Impact of FiT incentives on payback period for typical Solar PV system of (less than 10 kWp)...... 96

Figure 4.14 Grid connected PV system and its components (the picture inside the energy analysis room)...... 101

xiv

Figure 4.15 Schematic diagram of Grid-connected PV system ...... 102

Figure 4.16 Solar radiations and solar cell temperatures through different...... 103

Figure 4.17 Comparison between the HOMER models and the developed model ...... 104

Figure 4.18 Optimization results of the full load model which build in table categorizing and configuration models according to the best NPC...... 105

Figure 4.19 Optimization results of energy saving load Model which built in table categorizing and configuration models according to the best NPC...... 105

Figure 4.20 Relation between NPC of full load model and grid extension cost...... 106

Figure 4.21 Relation between NPC of energy saving load model and the grid extension ...... 107

Figure B1 Solar Radiation Mapping over Peninsular of Malaysia for January...... 196 Figure B2 Solar Radiation Mapping over Peninsular ofMalaya Malaysia for February...... 197 Figure B3 Solar Radiation Mapping over Peninsular of Malaysia for March...... 198 Figure B4 Solar Radiation Mapping over Peninsularof of Malaysia for April...... 199 Figure B5 Solar Radiation Mapping over Peninsular of Malaysia for May...... 200

Figure B6 Solar Radiation Mapping over Peninsular of Malaysia for June...... 201

Figure B7 Solar Radiation Mapping over Peninsular of Malaysia for July...... 202

Figure B8 Solar Radiation Mapping over Peninsular of Malaysia for August...... 203

Figure B9 Solar Radiation Mapping over Peninsular of Malaysia for September...... 204

Figure B10 Solar Radiation Mapping over Peninsular of Malaysia for October...... 205 FigureUniversity B11 Solar Radiation Mapping over Peninsular of Malaysia for November...... 206 Figure B12 Solar Radiation Mapping over Peninsular of Malaysia for December...... 207

Figure C1 Solar PV has been used in the present analysis in UMPEDAC building ...... 208

Figure C2 The controlling has been used in the present analysis in UMPEDAC building ...... 208

Figure C3 Grid connected components has been used, the picture show: (inverter, meter, and connecting switch)...... 209

Figure C4 The direct connected PC show the fluctuation in the DC voltage and current ...... 209

Figure C5 Underneath PV array data logger has sensor recording weather temperature and humidity...... 210

Figure C6 Wireless weather station from Davis Company has been used to transferee the weather parameter...... 210

Figure C7 List of Temperature sensors and Pyrometers have been used ...... 210

Malaya

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xvi

LIST OF TABELS

Table 2.1 Renewable Energy Tariff in Malaysia (Proposed for 2011) ...... 31

Table 2.2 Total FiT open from 2011 to 2014 under the quota-based mechanism ...... 32

Table 3.1 Geographical locations of the meteorological stations in Peninsular Malaysia ...... 51

Table 3.2 Type of activation functions in the artificial neural network...... 55

Table 3.3 Technical specifications of solar panel of grid-connected solar PV system ...... 60

Table 3.4 Range of capital-specific costs of solar PV based on the rated power...... 70

Table 3.5 Solar Energy tariff in Malaysia proposed for 2011 ...... 71

Table 3.6 Specifications of SPSL components utilized in the proposed models ...... 75

Table 4.1 Different network performance based on the training and testing datasets. 80

Table 4.2 Statistics of predicted monthly average solarMalaya radiation in Peninsular Malaysia 82

Table 4.3 Typical output of the developed solar PV system. 93 of Table A1 Solar radiation and clearness index prediction for month January...... 124

Table A2 Solar radiation and clearness index prediction for month February...... 130

Table A3 Solar radiation and clearness index prediction for month March...... 136

Table A4 Solar radiation and clearness index prediction for month April...... 142

Table A5 Solar radiation and clearness index prediction for month May...... 148

Table A6 Solar radiation and clearness index prediction for month June...... 154 TableUniversity A7 Solar radiation and clearness index prediction for month July...... 160 Table A8 Solar radiation and clearness index prediction for month August...... 166

Table A9 Solar radiation and clearness index prediction for month September...... 172

Table A10 Solar radiation and clearness index prediction for month October...... 178

Table A11 Solar radiation and clearness index prediction for month November...... 184

Table A12 Solar radiation and clearness index prediction for month December...... 190 xvii

Table D1 Results for survey question 1...... 214

Table D2 Results for survey question 2: ...... 214

Table D3 Results for survey question 3...... 214

Table D4 Results for survey question 4...... 214

Table D5 Results for survey question 5...... 214

Table D6 Results for survey question 6...... 215

Table D7 Results for survey question 7...... 215

Table D8 Results for survey question 8...... 215

Table D9 Results for survey question 9...... 215

Table D10 Results for survey question 10...... 215 Table D11 Results for survey question 11...... Malaya...... 216 Table D12 Results for survey question 12...... 216 Table D13 Results for survey question 13...... of ...... 216 Table D14 Results for survey question 14...... 216

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xviii

LIST OF SYMBOLS AND ABBREVIATIONS

ArcGIS geographical information system software

ANN artificial neural network

BIPV building integrated photovoltaic

Bio-GEN Biomass generation

CPV Concentrator photovoltaic technology

CDIAC carbon dioxide information analysis centre

CF Capacity Factor

CRF capital recovery factor

DARPA defense advance research project agency (in usa) DNIclear direct normal irradiance (cloud-freeMalaya sky condition) EIA energy information administration FIT feed- in-tariff of GDP gross domestic product

GHG greenhouse gases

GIS geographical information system

IEA international energy agency

IPCC international panel climate change

KeTTHA green technology and water kWhUniversity kilowatt-hour kWp kilowatts peak

LCOE levelized cost of energy

LM Levenberg-Marquardt learning algorithms

MEGTW ministry of energy green technology and water

xix

MTOE millions tons of oil equivalent

MAPE mean absolute percentage error

MAPD mean absolute percentage deviation

NPV net present value

NREL national renewable energy laboratory

NN neural network

NOCT nominal operating cell temperature

O&M operation and maintenance

PV photovoltaic

RE renewable energy RMSE root mean square error Malaya R correlation coefficient factor SCG Scaled conjugate gradient oflearning algorithms SEDA sustainable energy development authority

SPSL standalone pole street light

SREP small renewable energy programme

STC standard test conditions

Ppv power produce by solar pv

YPV rated capacity of the solar cell panel

2 GT,STCUniversity irradiance under stander test condition(w/m ) 2 GT total incident solar radiation(wh/m )

G global solar radiation (wh/m2)

2 Gsc solar constant (1367w/m )

Go extraterrestrial solar irradiance

Kt clearness index

 declination of the earth's axis through days of the year n any day in the year which has 365 days (starting 1st january)

 earth latitude

 hour angles

Tc,STC temperature of pv module under stander test condition fPV pv derating factor fc pv temperature coefficient

temperature coefficient of power (%/co)  p

 solar absorbance of pv array  solar transmittance of any cover overMalaya the pv array  pv efficiency under stander test condition STC of fv pv velocity coefficient

CAnn, Total total annualized cost of the system ($/year)

CCap system capital cost ($)

Com& operating and maintenance cost of energy ($/year)

f inflation rate

e inflation of escalation rate

e operating and maintenance escalation om&University i nominal interest rate

n project lifespan (year)

 discount factor

xxi

CHAPTER 1: INTRODUCTION

1.1 Background

We have believed that the civilization development is impossible without a sufficient energy supply, but if the fuel supply is finished, there will not only be no energy supply when the fuel is exhausted, but all the process that depends on it will stop. Nowadays, the consumption of fossil fuels is steadily increasing along with improvements in the quality of life, increase of the world population, which will not only lead to an increase in the rate of diminishing fossil fuel reserves but will also have a significant adverse impact on the environment related to greenhouse gas emissions into the atmosphere.

These deadly emissions represent a nightmare faced by developing countries, and are a destructive hazard to humanity as a whole and the natural environment as well. Therefore, many protocol and policy have been formulatedMalaya and challenges still making energy demand and greenhouse gases are theof extremely human concerns. Malaysia has a grand vision of becoming a developed country by 2020 (Prime

Minister's Office of Malaysia, 2010). As a developed country, Malaysia must abide by international conventions and obligations regarding the control of greenhouse gas emissions. Therefore, the government has formulated several climate policies to cut down greenhouse gas emissions. The target of these polices is to adopt an indicator for a voluntary reduction of CO2 by up to 40% in terms of GDP emission intensity by the year 2020University compared to the 2005 levels. This target is not achieved yet, rendering the greenhouse gas emissions and energy consumption sectors the two decisive challenges squarely facing Malaysia's vision of becoming a developed country by 2020 (Ahmad et al., 2011).

Based on the latest census in 2012, Malaysia has a population of about 29,337 million covering an area of 328,600km2. The economy recorded a growth in 2012, with the

GDP increasing at an average rate of over 5.6%. The total primary energy supply has increased steadily over the past 12 years and is estimated to reach about 83.9 MTOE by

2012, which is more than a 290% increase from 1990 (Figure 1.1) (Energy

Commission, 2012). However, the fuel consumption rose at an annual growth rate of

6.8% from 1990 to 2012 reaching 46.7 MTOE in 2012. The transportation sector has a lion share as an energy consumer with a record 17.18 MTOE in 2012 (Figure 1.2)

(Energy Commission, 2012), followed closely by the industrial sector with a record

13.91 MTOE, which is mostly powered by petroleum fuels (Qin, Chen et al. 2011).

100000 80000

60000 40000 (KTOE) 20000 Malaya 0 Kilo equivalenttons oil

Crude oil Naturalof gas Coal and Coke Hydro power Petroleum Product

Figure 1.1 Primary energy supply by fuel type in Malaysia

50000 40000

30000 20000 10000 (KTOE) 0

UniversityKilo equivalenttons oil Industry Transport Residential and Commercial Non-Energy Use Agriculture

Figure 1.2 Final energy consumption by sector in Malaysia

It is to be seriously noted from the above figures that the current energy indicator refers to the heavy dependence on non-renewable fuels -- coal, natural gas and crude oil, which will not only rapidly deplete the natural sources, but will consequently contribute to the unavoidable discharge of huge amounts of greenhouse gases. For example, the total carbon emission from Malaysia in 2006 was 171 million tons (6.48 tons/person assuming the total population of Malaysia was 26.3 million). This increased to approximately 217 million tons in 2010, an incredible increase of 46 million tons in the short span of 4 years (Figure 1.3) (Division, 2013).

250

200 150 (CDIAC)

100 2

CO 50 Malaya Million metric Millionmetric tons of 0

1990 1991 1992 1993 1994 1995 1996 1997 of1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Figure 1.3 Carbon dioxide emissions resulting from the consumption of oil, natural gas and coal Source: data resource (Division 2013).

To cope with energy and greenhouse gas challenges, it has recently been noted that mixing renewable energy in the country may be the best available choice to guarantee a safe, reliable and economic supply of energy, to mitigate the resulting climatic changes and strive to lower the carbon economy.

1.2University Global Environmental Concerns Most scientists worldwide agree that human activity involving traditional resources of energy induces climate change, which poses a serious threat to both societies and the earth’s ecosystems (Bolin and Dôôs, 1989; Schneider et al., 2002). A number of climate changes are evident due to greenhouse gases, including an increase in the earth’s temperature. The third assessment report (TAR) developed by the Intergovernmental

Panel on Climate Change (IPCC) mentions that “Continued greenhouse gas emissions at or above current rates will cause further warming and induce many changes in the global climate system during the 21st century that would likely be larger than those observed during the 20th century” (Meehl et al., 2007). However, according to multi- model results developed through unprecedented joint efforts of many scientist groups around the world, and based on current emission scenario trends it has been concluded that the average global temperature is expected to rise by 1.4°C to 5.8°C between 1990 and 2100 (Meehl et al., 2007). This could result in severe climate parameter changes

(e.g. rising sea levels, decreasing snow and ice extent, etc.). Besides, there would be an impact on food, agriculture, manufacturing and prices, water availability, nutrition and health status. It is difficult to predict the indirect resultant risks, but the impact may be the worst (McMichael et al., 2006). In the context Malayaof controlling such adverse climate change effects, a number of institutions have been created to address these issues. The United Nations Framework Convention onof Climate Change (UNFCCC) was created in 1992 to provide a framework for policy making to mitigate climate change. In 1997, the

Kyoto Protocol was formed as a keystone document in international climate change policy to reduce greenhouse gas (GHG) emissions and adapt to climate change

(Protocol, 1997). Many protocols and policies have been formulated and there are still challenges making energy demand and greenhouse gases extreme human concerns, especially in developing countries.

MalaysiaUniversity ratified the Kyoto Protocol on September 4th, 2002, to control and reduce greenhouse emissions (Fadare, 2010). Considering the world as a whole, global energy scenarios and the increase in greenhouse gases, the following works more or less highlight this issue from Malaysia’s monitoring perspective since Malaysia has a grand vision of being a developed country by 2020, consequently reflecting a rise in energy demand and greenhouse gas emissions. It is worth noting that climate change and

4 melting polar ice caps will cause a global rise of sea levels, reshaping all coastal regions of the world, not to mention the massive flooding of food-producing, low-lying coastal areas besides the very long coastal areas of Malaysia.

1.3 Advantages of Solar Energy

Renewable energy holds huge promise for the near future because it is undeniable that it provides unlimited sources of energy. The solar source is not only environmentally friendly but also immensely renewable as compared with traditional sources, such as fossil fuel with all its adverse environmental effects. The modality and simplicity of this technology, and very low maintenance cost within the project lifespan as well as environmental benefits can progressively mitigate the effects of greenhouse gas emissions. All these pragmatically visual benefits have significantly made positive inroads to further enhance the alluring trend of solarMalaya PV technologies to be positively acceptable, hence conductively enabling a substantial contribution to the world’s clean energy needs. It is now timely indeed thatof the different forms of incentives are to be vigorously promoted for the global acceptance of renewable energy in many parts of the world with gainful incentives in different forms, such as investment, policies and production incentives.

Compared to other countries such as Germany, Malaysia has more favorable solar radiation-receptive locations for the development of solar energy harnessing plants. In

Germany the cumulative solar PV installation capacity was 17GW in 2011, which is moreUniversity than half the worldwide installation capacity with a buyback period ranging between 3 and 10 years (Jacobs and Sovacool, 2012). However, the CO2 emissions reduction in Germany owing to renewable energy applications is approximately 22.2% compared with 1991, and there are 367,400 people working in the field of renewable energy (Energy Commission, 2012).

The solar radiation intensity in Malaysia is greater than in Germany, whereas other countries frequently experience unfavorable seasonal changes in solar energy output and Malaysia has the overriding advantage of only one season. However, Malaysia is suitable for solar PV implementation due to the availability of sunlight, which is more than 10 hours daily, and it is possible to have about 6 h of direct sunlight with irradiation of between 800 W/m2 and 1000 W/m2. In addition, the capabilities of thin film technology solar cells to work under low light condition make photovoltaic usage more efficient and more power could be obtained from sunlight (Amin et al., 2009). The solar radiation advantages in Malaysia are numerous. Firstly, the high intensity of diffuse radiation components compared with beam solar radiation makes the topographical surfaces more receptive to solar radiation regardless of surface orientation by virtue of the fact that Malaysia is situated at theMalaya Equator. Secondly, buildings are designed and constructed to be conductively advantageous for PV integration particularly the roofs, which have fewer windowsof to prevent solar heat from entering the structures while wide open surfaces are freely available for PV installation. Thirdly, buildings constructed by the Housing Development Board are typically assembled to allow for the installation of PV sets of relatively the same size and that can be efficiently applied in large numbers without requiring costly modifications. Fourthly, the maximum temperature in Malaysia is roughly 34oC during the day year-round, which means solar cells do not get damaged from extremely high temperatures as they wouldUniversity in the Middle East. Solar PV panels can be used for heating water utilizing PV/T, and most people in Malaysia use water heaters. Last but not least, the grid-connected program supported by the Feed-in-tariff mechanism will reduce the solar PV cost and cut down on solar PV components that are sometimes more costly and need to be changed from time to time, e.g. batteries.

1.4 Problem Statement

Despite all the mentioned positive facts and benefits that can be conveniently derived from using solar PV energy in Malaysia, reports indicate there is a chronic shortage of solar-energy implementation (EU-Malaysia Business, 2013). This is mostly owing to the inaccurate or sometimes misleading perceived knowledge about solar-energy resources as well as the cost and performance of photovoltaic solar power generation.

These facts primarily render most Malaysians unaware of the government’s incentives and policies towards renewable energy; thus they are not willing to invest in the feed-in tariff scheme, significantly impeding the adoption of solar-energy technologies

(Muhammad et al., 2011). Another proven key issue was raised by Banks regarding the delay of approvals for renewable-energy applications. Banks also admit there is a financial knowledge gap when it comes to evaluatingMalaya green technology projects. Meanwhile, the delay in establishing the Feed-in tariff (FiT) in Malaysia and uncertainty of the returns on investments fromof the FiT provided little comfort to banks when assessing and approving loans (EU-Malaysia Business, 2013). Hence, there is a need to evaluate green technology projects, a matter that stresses the purpose from the following work. In this context, there is an urgent need for a comprehensive assessment of grid-connected PV applications to provide information on solar radiation resources using neoteric techniques such as Artificial Neural Networks. However, the performance and level cost of energy analysis of solar PV power generation should be wellUniversity-documented. In addition, there is a need to analyze the public level of awareness regarding the adoption of solar PV energy through government incentives, their willingness to pay for clean energy, and acceptance of renewable energy. This is important to improve regulatory and policy initiatives designed to promote the implementation of renewable energy.

1.5 Thesis Objectives

This contemporary study is developed to enhance solar PV application in Malaysia, which can be achieved by generally formulating the following targets.

 To develop solar radiation maps and tables based on modeling solar radiation

using neoteric Artificial Neural Network models. The developed maps should

consider the impact of the complex topography in Peninsular Malaysia, and the

databases are very important to visualize the potential of solar radiation

resources and optimize solar conversion systems as well.

 To model a grid-connected solar PV system in order to provide full information

regarding the performance and up-to-date grid-connected solar PV electricity

generation costs, and study the potential benefits from the feed-in-tariff mechanism which are critical to beneficiariesMalaya such as investors, financiers, and home owners, who are very willing to implement investment plans in the field of

alternative energies. of

 To conduct sensitivity analyses on the performance of grid-connected PV

systems and the impact of the most important solar PV parameters on energy

and the unit cost of energy.

 To model a solar PV power stand-alone street light application system in a

remote area. The aim is to provide complete information regarding the

Universityperformance and system electricity generation costs in order to outline financing

options that could assist with PV system implementation.

 To conduct a survey to critically analyze the social acceptance regarding solar

PV applications along with government incentives. The problem and obstacles

are highlighted from several aspects to improve regulatory and policy initiatives

designed to promote renewable energy implementation.

1.6 Scope of the Study

Based on the targets of the following work, the scope of the study comprises:

 The long-term database of 24 ground meteorological station parameters will be

used to improve ANN learning and training, with the best model being used to

characterize solar radiation in more than 341 cities in Peninsular Malaysia

taking into account the impact of adverse terrain environments.

 The ArcGiS9 will be used to visualize potential locations for solar energy

application using ANN-predicted data, thus increasing the resolution of solar

maps and highlighting solar energy locations in Malaysia based on 12 monthly

maps. The database of predicted solar radiation and clearness index for 341

locations will be documented.  A pilot PV system for research purposes willMalaya be established in 24 locations, have different topography and solar resources, to investigate the real energy production and system cost of grid-connectedof PV applications and study the impact of the Feed-in tariff on capital cost and payback period. Such

information is important for the faster application of grid-connected solar PV

under the Feed-in tariff.

 Investigate the impact of different technical and financial key parameters on

energy to show how much the level cost of energy can be increased or decreased

through changes in PV parameters. This will be done using the HOMER Universitysensitivity analytical tool for the capital of Malaysia, Kuala Lumpur.  Develop an experimental study in the UM Power Energy Dedicated Advance

Centre (UMPEDAC) building to investigate the most proper derating factor

value that measures the difference between the power produced by a solar array

in the lab to that produced for the same array in the field, and use it for energy

calculation. This will minimize PV energy estimation error and facilitate

studying the performance.

 The current study was extended to investigate the feasibility of solar energy for

standalone solar street light application in remote areas in an attempt to produce

a new investment area. In this context, we will select one location that has the

most potential for solar energy application, and two models will be developed.

 Investigate the public’s perception and expectations regarding solar energy

usage and their concept about the Feed-in tariff mechanism. The scope is also to

find out all the causes and obstacles that make people unaware of the

government’s incentives in Malaysia and then suggest a new, modified current

solar energy policy based on the new findings and survey established by the current study. Malaya 1.7 Significance of the Study

 Solar radiation maps are developedof using ANN models to identify the

appropriate locations for solar energy development. These locations are

potentially critical to beneficiaries such as designers, investors, financiers and

home owners that are ever so willing to invest in the field of procuring

alternative sustainable energies. Furthermore, the solar maps could be

significantly useful for global comparison and to provide a source for

stakeholders as an optional long-term plan in terms of exploiting solar energy as

Universityan alternative source of energy.

 The comprehensive assessment will facilitate an effective demonstration of the

techno-economic viability, design, development, financing and sustainable

operation of grid-connected power generation and generation projects. This is

important to foster the application of renewable energy in Peninsular Malaysia

to cut down electricity cost, and reduce the pollution levels and global warming

resulting from the use of conventional energy sources.

 This work represents an emphasis on the current energy scenario and related

development policies in Malaysia. It also provides an up to date analysis of the

costs of generating electricity from solar PV, which will allow a fair comparison

of solar PV with other generating technologies in Malaysia and the rest of the

world. Moreover, the study could also serve as a general tool to support decision

making in assessing the application potential of various clean energy

technologies for the power supply of large-scale manufacturing facilities and

also help formulate any future greenhouse policies regarding clean technology.

 Last but not least, this work represents a qualitative and quantitative study. It is expected that this work will enrich the knowledgeMalaya perceived by Malaysians about the cost and performance of grid-connected solar power generation and encourage the implementation ofof solar energy. The developed survey and suggestions will strengthen any future formulation of policies and regulatory

frameworks to promote the wide-spread adoption of RE projects. The

government and financial community should benefit from sufficient information

to assist the development of sustainable RE financing.

1.8 Thesis Outline

CHAPTER 2: This chapter includes the literature review of this dissertation and will giveUniversity an overview of the present status of renewable energy policy formulation and initiative programs that support renewable energy and mitigate greenhouse gas issues in

Malaysia. The gap regarding solar resources in Peninsular Malaysia is assessed, and an explanation is given on how the artificial neural network has been used in the domain of solar energy applications. This chapter also discusses technical and economic analysis of recent solar PV research in Malaysia, and in several different locations in the

11 worldwide. However, it reviews the current PV power supply technologies besides modeling tools and software utilized for comparison and sensitivity analysis. Also, this chapter will discuss social acceptance as an important subject that may help with the extensive implementation of renewable energy technologies in Malaysia. This chapter concludes with a critical review of the chapter highlights.

CHAPTER 3: Chapter three presents the study methodology. Four methodologies have been developed in the present work. The first is using artificial neural networks for solar resource assessment (solar radiation modelling and prediction, and solar resource mapping). The second methodology uses the feasibility of grid-connected solar PV systems and assumptions (modeling solar radiation resources, e.g. solar radiation, clearness index, extraterrestrial radiation; modeling energy and cost of grid-connected solar PV power). Third is the methodology of usingMalaya solar PV-powered stand-alone street light applications in a remote area due to grid extension cost. Fourth is the methodology of investigating the social acceptanceof issues of solar energy in Malaysia and people’s background of the feed-in tariff mechanism is presented.

CHAPTER 4: Chapter four comprises the results and a discussion. This chapter is classified into six parts, beginning with the introduction. The second part is related to the output of methodology one, whereby the solar energy potential in Peninsular

Malaysia is discussed based on the developed maps and accuracy of solar radiation data predicted using ANN. The third part addresses a technical and financial analysis of grid- connectedUniversity solar photovoltaic (PV) systems in Malaysia based on photovoltaic key parameters (e.g. energy output, payback period based on the feed-in tariff, levelised cost of energy, greenhouse reduction). In the fourth part, a sensitivity analysis of different

PV parameters on the levelised cost of energy is developed due to the different market options and uncertainties in the key solar PV parameters. The fifth section discusses the possibility of using solar energy for street light application to create investment in this

12 domain. In the sixth part, the social acceptance issues of solar energy in Malaysia are analyzed based on a public survey and the most important obstacles faced by people regarding solar PV application. The success of the education channel of government representatives through the media or other means is measured with respect to creating awareness among people in Malaysia regarding solar PV application and incentives under the feed-in tariff.

CHAPTER 5: This chapter concludes the dissertation with a policy recommendation.

The main outcomes of both solar energy potential and clearness index are presented in the form of database tables and solar maps which clarify the potential of solar resources.

Moreover, the solar energy perspective for sustainable development in Peninsular

Malaysia is highlighted with outcomes of the suggested models. The specific objectives are presented, which help decide whether to implementMalaya these systems or not. Lastly, the survey results are discussed and suggestions are formulated for the current and future policies. of

University

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

Sun power is colossal, besides easy to access and harvest. Acquiring and compiling solar radiation resources are a central point for a thoughtful and durable approach of energy development. In this unique case, there is a convincing need to model solar energy as a fuel source to apply in a solar energy conversion system. This can be achieved by having information on the amount and quality of the incident solar radiation so as to enable the process of solar conversion system optimization. However, there is also a need to model the application of renewable energy to motivate the

Malaysian government on its target of using solar PV energy for sustainable development based on the feed-in tariff or any other support mechanism. There is a further need to identify the obstacles to the government’sMalaya incentives and policies on renewable energies. Thus, the challenges are evidently portrayed through this thesis with the unenviable facts mentioned in of the problem statement and the diversity of emphasis targets in the present work. This literature review is concisely divided into relevantly sensible sections, as more time will be devoted to the framework taking into account the multiplicity of targets. The literature review sections are as follows:

Sections 2 represent an outline of solar PV applications and modeling documented in

Malaysia and elsewhere. Section 3 provides an overview of green energy resources and modeling,University and the best predicting methods used for solar energy are emphasized accordingly. The Artificial Neural Network and its application in the solar energy domain are highlighted as well. Section 4 represents an overview of the environmental and related policies currently enforced in Malaysia. The incentives and other kinds of financial support, such as the Feed-in Tariff are appropriately highlighted as and when necessary. Section 5 covers an overview of clean technology as a valid possible choice for sustainable energy development. It is shown that the best developed methods and

14 software can be used to advantageously assess the technicality and economy of renewable energy projects. In Section 6 the social acceptance criteria applied elsewhere are defined and reviewed. Finally, Section 7 provides a critical literature review for the above sections.

2.2 Review of Solar Resource Assessment Technique

For decades, solar power has been recognized as the ultimate source of all energy sources, such as traditional fuel or food fuel for our bodies. Solar power is immense and furthermore, it is easy to access or harvest. In this unique case, there is a compelling need to model a solar energy conversion system. However, this only represents the first half of the equation whereas the second half is to identify the appropriate locations for solar energy development that would be potentially critical to beneficiaries, among which designers, investors, financiers, and home Malaya owners, all of whom are ever so willing to invest in the field of procuring alternative sustainable energies. Their decisions would be based on crucially confirmedof information on the quantities and locations where these energies are abundantly available. Hence, reliable forecasts of solar power are important for the efficient integration of large shares of solar power into the electricity supply system (Myers, 2012). Of course there are other problems yet to be overcome, such as those related to the reliability of available resources. Hence, methodically acquiring and compiling solar radiation resources is the central point of focus for a viable and durable approach of sustainable carbon-free energy development

(RanchinUniversity et al., 2002).

In some countries, solar resource data are difficult to acquire and compile. It involves lengthy periods of up to several years to perform realistic evaluations, not to mention the additional cost of installation, maintenance and quality control that may cover large areas of unfavorable terrain and hostile climate conditions. Thus, attempts have been ongoing to exploit available information to explore new locations with acceptable

15 accuracy. In existing literature, different kinds of databases based on solar radiation are documented, such as meteorological stations, satellite image databases and the National

Weather Prediction (NWP) (Lorenz and Heinemann 2012). Several models for solar radiation have been performed to extrapolate solar radiation based on the different kinds of weather parameter databases. These models have been critically reviewed in many studies, including (Inman, 2013; Bakirci, 2009; El-Sebaii et al., 2010; Lorenz and

Heinemann, 2012; Besharat et al., 2013; Yadav and Chandel, 2013). Models are categorized in three groups:

The first is an empirical entity based on meteorological parameters employed as the model input. The most popular one is Angstrom equation. This equation is empirical relation between solar radiation and hours of sunshine. The original type of this equation is given by the following formula: Malaya

퐻 푛 (2-1) = 푎 + 푏 퐻표 푁 of

Where, a and b are regression parameters depend on the latitude. While, n is hours of bright sunshine, and N is the total hours of sunshine (from dawn until sunset). However,

H is the total solar radiation on a horizontal surface, and Ho is extraterrestrial solar radiation on the horizontal surface (solar radiation without atmosphere). Angstrom equation has modified many times by several researchers to be suitable for their locationsUniversity (Bakirci, 2009).

The second is the data mining model, such as the Artificial Neural Network (ANN) or

Fuzzy Logic, which are among the most powerful tools available for detecting and describing subtle relationships in massive amounts of seemingly unrelated data. Each developed model is defined by its intrinsic characteristic accuracy.

The third is the satellite database such as available on https://eosweb.larce.nasa.gov/.

These approaches depend on satellite observations, which are developed under NASA program. NASA data is sensitive to climatic conditions in the regions. Such as (1) predominant cloud types may be different (thicker or thinner), (2) atmospheric aerosols may be more or less absorbing, (3) surfaces may have large differences in albedo, and

(4) seasonal wind patterns may transport significant pollutants either in or out of the region. Ignoring regional differences has resulted in surface insolation bias errors as large as 35%. However, the data are considered to be the average over the entire area of the cell (50mm*50km grid system), hence did not take the impact of the terrain such as our model. For this reason, these approaches are not intended to replace quality ground measurement data. However, its purpose is to fill the gap where ground measurements are missing, and to augment areas where ground measurementsMalaya do exist (SSE, 2014). In Malaysia, a limited number of studies have been conducted to evaluate and model solar radiation. The majority of models useof the Linear Angstrom regression equation, which is normally used for specific sites and cannot be used to extrapolate the data for new locations with difficult topography such as Malaysia. For example, Chuah and Lee evaluated the global solar radiation for 9 towns in Malaysia that was gathered from sunshine data using the simplest linear relationship between global solar radiation and sunshine duration as developed by the Angstrom equation (Chuah and Lee, 1981). The coefficients of this relationship (a, b) are obtained by linear regression method using dailyUniversity radiation and sunshine data. The sun duration data was available for 12 towns in

Peninsular Malaysia for the period 1961-1975. The calculation shows that the linear correlation tends to be poorer for a month in which extreme high and low daily sunshine durations occurred frequently. However, the study found levels of solar radiation between 3.5 and 6 kW/m2/day in northern and east-coast towns and between 4 and 5 kW/m2/day in southern towns. After 1 year, the same authors studied the characteristics

17 of solar radiation in Peninsular Malaysia for 3 cities, namely Kota Baru, Kula Lumpur and Penang, using a standard form of the Angstrom regression equation. The study indicates that days with low daily total radiation occur scarcely in Penang and Kuala

Lumpur, and Penang’s days have high radiation that occurs more frequently than in

Kuala Lumpur. Kota Baru exhibits larger variability of daily total radiation. Extreme low radiation for long periods occurs frequently during the north-east monsoon period, while high radiation for long periods occurs during the dry season (Chuah and Lee,

1982). Hu and Lim studied the variation of solar radiation using Linear Angstrom regression equations for 14 towns in Peninsular Malaysia. They found similar seasonal variation in the northwest of the peninsula, ranging from up to 6 kW/m2/day in April to a relatively low value of 2.5 kW/m2/day in northeast towns in December. They also determined that the variation in global solar radiationMalaya most correlate with the local land/sea breeze and topography (Hu and Lim, 1983). Sopian and Othman studied the monthly average solar radiation incident onof a horizontal surface for 8 cities using the Angstrom equation. The cities are Bandar Baru Bangi, Kuala Lumpur, Petaling Jaya,

Senai, Bayan Lepas, Kota Baru, Kota Kinabalu and Kuching. The monthly average value for the 8 cities ranged between 3.3 kWh/m2/day in Kuching and 5.5 kWh/m2/day in Bayan Lepas. Nonetheless, the study showed that the developed Angstrom models can be used for solar energy application in these locations (Sopian and Othman, 1992).

Zaharim et al. used a two-model, multiple linear regression and time series to forecast monthlyUniversity solar radiation in the city of Bangi, Malaysia. The Jenkins method was applied to modeling solar radiation data and the study indicated that the model is adequate to develop a time series of solar radiation data (Zaharim et al., 2009). Muzathik et al. studied some aspects of solar radiation climatology for Terengganu in Peninsular

Malaysia. According to the study, the highest monthly mean daily global solar

18 irradiation value was 6.5 kWh/m2/day; meanwhile, the highest hourly average solar irradiance intensity during the study period was 1139 W/m2 (Muzathik et al., 2010).

The gaps identified are that the empirical models developed by these studies are location-specific as well as limited in scope and application. The empirical models were for no more than 14 cities and did not take the impact of topography in consideration.

Therefore, these models (Angstrom model) cannot be used to extend solar radiation prediction, because the complex terrain and mountain range in Peninsular Malaysia will lead to significant error along with costly failures. For instance, the mountain range in

Peninsular Malaysia may cause clouds to form preferentially aligned with the mountain ridges or there may be a land/sea boundary (Hu and Lim, 1983). Although the above empirical models are suitable for solar radiation application in the developed sites, they are in fact unsuitable for extending the predictionMalaya of solar radiation data and hence cannot be used to develop any solar radiation maps for Peninsular Malaysia or for solar radiation application in faraway locations.of In this context, there is an urgent need to develop a new model for solar radiation in Malaysia to overcome the terrain challenges and predict a new data point for solar radiation. These issues will be mentioned in methodological detail in the present work.

2.2.1 Solar Radiation Prediction using ANN

The artificial neural network (ANN) is one of the most common methods in Data

Mining and is capable of performing predictive data analysis. Lately, ANN has been usedUniversity by many researchers for solar radiation data predictions using weather geographical parameters as input. Several researches and different articles have shown that the ANN is good at fitting functions and reorganizing patterns, and has also been trained to solve problems that are humanly difficult to perform even using conventional computers (Demuth, Beale et al. 2008). Hence, it has been used in many other studies that have analytically and scientifically been reported by DARPA (Ukil, 2007). The

19 general steps for solar radiation prediction through ANN using Matlab tool is shown in

Figure 2.1.

Figure 2.1 Pipeline for solar radiation prediction through ANN

In literature, many solar radiation prediction models are being developed using ANN for hourly and monthly basic readings due to the increasing number of solar radiation systems, which has boosted the importance of solarMalaya radiation prediction. Researchers often use this technique with deferent parameters for input, such as: longitude, latitude, number of days, temperature, altitude, humidity,of average diffused radiation, mean beam radiation, month, humidity, relative humidity, sunshine hours and wind speed, wave length and rainfall. As mentioned earlier, neural networks are good at fitting functions.

As a matter of fact, it has been proven that a fairly simple neural network can fit any practical function (Fausett, 1994). Herein, the monthly and hourly solar radiation prediction estimated by ANN and other comparative models is discussed. SözUniversityen and Arcakliogˇlu (2005) studied the monthly global solar radiation in Turkey using the artificial neural network. Two different models were developed to train the neural networks. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) were used in the input layer of the network, and relative humidity values were added to one network in the model

(Model-2) to examine the impact of the humidity parameter. Scaled conjugate gradient

(SCG) and Levenberg-Marquardt (LM) learning algorithms together with a logistic

20 sigmoid transfer function were used in the network. Meteorological data for 4 years,

2000–2003, and 18 cities spread over Turkey served as data in order to train the neural network. Apparently, the best approach was obtained by the SCG algorithm with 9 neurons for both models. However, the maximum mean absolute percentage errors

(MAPEs) were 2.77% and 2.85% for Model-1 and Model-2, respectively, while the minimum MAPEs were 1.055% and 1.041% respectively. The results signified that humidity had only negligible effect on the prediction of solar potential using artificial neural networks. The overall results confirmed the capability of the ANN models to accurately predict solar radiation values.

Artificial neural network predicted the monthly global solar radiation in 8 typical cities in China (Jiang, 2009). Both feed-forward and back-propagation algorithm were applied in the analysis. The input parameters were latitude,Malaya longitude, altitude, and sunshine percentage. The proposed ANN model and the other 7 empirical regression models were compared with each other. The resultsof of the ANN model and other empirical regression models have been compared with measured data on the basis of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE).

The ANN model is tested to predict the same components for Kashi, Geermu,

Shenyang, Chengdu and Zhengzhou stations over the same period. It is found that ANN has the minimum MPE of 0.48% and RMSE of 0.867. So comparison results indicate that ANN model provides the best results among the seven models. These results testify theUniversity generalization capability of the ANN model and its ability to produce accurate estimates in China.

ANN has been also used for hourly solar prediction forecasting purposes as well. For example, ANN-based 24-hour forecasting of solar irradiance was developed by Adel

Mellit and Pavan (2010) from Trieste, Italy. The input factors were the solar irradiance, the number of days in a month and mean air temperature per day, whereas the output

21 gave the parameters of the 24-hour solar irradiance for the following day. The outcomes illustrate that the correlation coefficient (R2) for sunny days was between 98% and 95%, indicating satisfactory results; nonetheless it was also a suitable technique for cloudy conditions since the correlation coefficient found was between 92% and 95%.

Ji et al., (2011) established a new hybrid method comprising two phases to foretell hourly solar radiation series. The new hybrid system that combines the Autoregressive and Moving Average (ARMA) with the Time Delay Neural Network (TDNN) was employed for phase prediction. The records were used for S mean series from February

2009 with sampling intervals of 10min. To calculate the performance of diverse models using the proposed model, the ADF test, RMSE and NRMSE were utilized. An ADF trial was performed to calculate the motionless phase of the detruded series, where the NRMSE and RMSE helped find the quality of theMalaya curvature fitting for the different models. The associated functions of all models were maintained in parallel with that of the proposed model. According to the outcomes,of the hybrid model performed with the best accuracy and stability. The overall conclusion is in favor of its good performance, considering that the solar radiation series consists of both linear as well as nonlinear components.

In Singapore, Wu and Chan (2013) employed a new multi-model prediction framework

(MMF) to estimate hourly solar radiation. The MMF functions in two phases, namely clustering and prediction. Performance prediction is greatly dependent on the precision of Universityclustering. The time delay neural network (TDNN) was applied for phase prediction.

To authenticate how MMF functions, Singapore solar radiation statistics from January

2009 to December 2010 were taken into account and the outcomes showed promising performance. The results were compared with other prediction models, for instance

ARMA, TDNN and the hybrid model, and it seemed that MMF was superior with

22 attained consistency and accuracy as well as standard mean deviation of maximum

94.8% error.

Hasni et al., (2012) used a back propagation neural network with the input of month, day, hour, temperature, and relative humidity values. In the study, the data was divided into parts to train the network, where the first part, consisting of 2824 patterns from

February 2 to May 31, 2011, was used for network training and the second part, consisting of 651 patterns from June 1 to June 28, 2011, was used to test the trained network. The results signify that the output for the predicted solar global radiation values was very close to the measured values for the period between February 2 and

May 31, 2011. This shows good agreement between the prediction and measurements on an hourly basis with the proposed technique.

In the work proposed by Benmouiza and CheknaneMalaya (2013), hourly global horizontal solar radiation was based on the combinationof of an unsupervised k-means clustering algorithm and artificial neural network (ANN). In their work, two global horizontal solar radiation time series were selected for simulation purposes. Prediction accuracy evaluation was obtained by calculating the root mean square error (RMSE) and the normalized root mean square error (NRMSE), where both methods allowed for a term- by-term comparison of the actual difference between the predicted and measured values; the results were compared with the ARAMA empirical model. According to the results,University the forecasted series using the proposed method was virtually the same as the tested one with RMSE of 60.24Wh/m2 and NRMSE of 0.1985, which are considered good forecast values compared to the ARMA model with NRMSE of 0.3078.

Chen et al., (2013) proposed a model based on the feed-forward Neural Network (FFN) with fuzzy logic for every hour in a day, i.e., from 08:00 to 18:00. In this study, 3 sky conditions, namely sunny, rainy and cloudy, were classified for each hour. To find the

23 sky condition for low, average or high, the three usual average hourly temperature values were selected for each sky condition of every hour. The grouping procedure was enhanced and the reduced sky classes were found by applying fuzzy logic with ANN.

The Mean Absolute Percentage Error (MAPE) from the proposed system was 9.65%, which was lower than the other methods with respect to solar radiation forecasting. The results determined that the radiation forecast outcomes were much more precise and superior when the proposed method was employed.

2.2.2 Solar Radiation Mapping

Using solar maps is one of the best ways to visualize solar irradiation results. However, in many fields that apply empirical data, there is a need for interpolation from irregularly-spaced data to produce a continuous surface. These irregularly spaced locations, referred to as "data points," may have diverseMalaya meanings; in meteorology, they mean weather observation stations; in geography,of they mean surveyed locations; in city and regional planning, they mean centers of data-collection zones, etc. In order to display these data in some types of contour maps or perspective views, it is extremely useful, if not essential, to define a continuous function fitting the given values exactly

(Shepard, 1968).

Solar irradiation mapping by interpolation/extrapolation of measurements is possible but leads to large errors except if the network is sufficiently intensive. For example, JournéeUniversity and Bertrand (2010) noted that an informative data-deficient station might strongly affect the final map. This is confirmed by other researchers such as Perez,

Perez and Seals (1994, 1997) who studied the extrapolation and interpolation errors as a function of the distance to the measuring stations. The errors were found to be very similar for both cases, though the interpolation errors were lower for shorter distances and vice versa. In this context, there is a need to increase the intensity of solar radiation

24 data using neoteric techniques such as Artificial Neural Networks with acceptable error in order to make any developed contour map more reliable.

ArcGIS, developed by the Environmental System Research Institute (ESRI), is the best tool available to date for developing solar radiation maps. However, it can serve many purposes, including compiling, e.g., geographical data, analytical mapped information, etc. ArcGIS consists of three components: Arc-Catalog, Arc-Map and Arc-Toolbox.

Arc-Catalog is used for browsing maps and spatial data, exploring spatial data, viewing and creating metadata as well as managing spatial data (Schneider et al., 2002).

However, it is built with a different method, which can be used to smooth the developed surface for cells in raster, such as Inverse Distance Weight (IDW); Kriging; Natural neighbor; and the spline interpolation method. It can help estimate parameter point data for any location on the developed surface, such as Malayaelevation, chemical concentrations, rainfall, noise levels and so on (Colin, 2004).

Globally, ArcGIS has often been employedof in several locations to develop solar radiation maps, clearness maps and wind energy maps based on the meteorological or satellite database used. In Oman, for instance, it was used for developed monthly average daily solar radiation and clearness index contour maps over Oman with metrological data from 25 stations (Al-Lawati et al. 2003). In Indonesia, the NASA satellite database was utilized to develop a solar over the country using the geographical information ArcGIS and artificial neural network to increase the special data distributionUniversity through the new data prediction (Rumbayan et al., 2012). In Nigeria,

ArcGIS with ANN was used to increase the special distribution of meteorological data and the NASA database served to develop solar radiation maps and wind maps for the country (Fadare, 2009, 2010). Likewise, in Turkey, sunshine duration was applied to develop a solar radiation map. In that study, 159 locations were predicted for developing solar radiation maps over the country (Bosch et al., 2008). In the present

25 work, ArcGIS is used to develop monthly solar radiation maps over Peninsular

Malaysia using ANN-predicted data.

2.3 Review of Grid-Connected Solar PV Applications

Grid-connected solar PV has a potential to share the energy production if subsidized well. For example, in Indonesia study showed that the entire potential of grid-connected

PV systems is about 27 GWp, generating 37 TWh/year, which is about 26% of the total electricity consumption in Indonesia over 2010 (Veldhuis et al., 2013). Numerous large- scale PV grid connected projects are currently being commissioned, with more plans for the near future (Eltawil et al., 2010).

Elsewhere, attempts were made to study the visibility of grid-connected PV systems.

For instance in Greece, the potential of a PV system with a rated capacity of 20 kW was examined. Technical, economic and environmental Malayaperformance was established at 48 stations in the country. HOMER softwareof was used during the analysis. The author found that the annual global solar radiation over Greece varied between a minimum of

4.04 kW/m2/day and a maximum of 4.89 kW/m2/day. The renewable energy produced every year based on solar PV (mono-crystalline silicon type with 14.5% efficiency) varied between 33.35 MWh/year and 41.63 MWh/year. The capacity factor varied between 19.4 and 24.2%, and the cost of electricity ranged between 0.122 €/kW (0.167

$/kW) and 0.152 €/kW (0.208 $/kW) from the most appropriate location to the least attractiveUniversity location (Fantidis et al., 2013). A feasibility study based on the Feed-in tariff mechanism of a grid-connected photovoltaic system was carried out in Istanbul, Turkey. The output power and temperature data were collected from PV modules and analyzed to determine solar power generation. It was found that customers could see reduced electricity payments by more than 40% by installing solar PV systems. Moreover, PV systems had the ability to meet the increase in power system demand through July (Batman et al., 2012).

In Saudi Arabia, the potential of PV systems with a rated capacity of 5 MW was examined. The feasibility of the project was established in 41 locations in the country.

RetScreen software was used during the analysis. The author found that the annual global solar radiation over Saudi Arabia varied between a minimum value of 4.46 kW/m2/day and maximum of 5.64 kW/m2/day while the capacity factors varied between

18.7% and 28.3%. It was found through this study that the mean internal rate of return

(IRR) value ranged between a minimum of 10.73% and maximum value of 13.53%.

Furthermore, 8182 tons of greenhouse gases can be prevented from entering the local atmosphere each year by installing 5 MW PVs in any part of Saudi Arabia (Rehman et al., 2007).

Grid-connected solar PV with a capacity of 1 MW was established at 14 stations in Bangladesh. HOMER software was utilized for the analysis.Malaya It appeared that the annual global solar radiation over Bangladesh varied between a minimum value of 4.51 kW/m2/day and maximum of 4.99 kW/m2of/day based on the Geospatial toolkit, NASA solar radiation database. The renewable energy produced each year via solar PV mono- crystalline silicon type had 13.9% efficiency, and varied between 1653 MWh/year and

1844 MWh/year. The capacity factor varied between 18.8% and 21%, and the cost of electricity fluctuated between 13.25 BDT/kW (0.17 $/kW) and 17.78 BDT/kW (0.23

$/kW) from the most appropriate location to the least attractive location respectively.

As such, at least 1423 tons of greenhouse gases can be prevented from entering the local atmosphereUniversity each year by installing PVs with 1 MW capacity in any part of Bangladesh (Alam and Sadrul, 2011).

Evaluation and modeling studies of grid-connected solar PV applications in Malaysia are limited and there are no extensive studies that cover all of Malaysia. Admin et al. studied practically the performance of different solar PV cells at UKM University in

Malaysia. The authors found that the weather in Malaysia is suitable for solar PV

27 implementation due to the availability of sunlight, which is more than 10 hours daily.

However, they said “it is possible to have about 6 h of direct sunlight with irradiation of between 800 W/m2 and 1000 W/m2, and it is already very good to consider for photovoltaic usage. In addition, the capabilities of thin film technology solar cells to work under low light condition make photovoltaic usage more efficient and more power could be obtained from sunlight" (Amin et al., 2009). Seng et al. investigated the feasibility of PV under the Malaysia Building Integrated Photovoltaic program

(MBIPV). The study was developed in 8 locations in Malaysia, which means the authors covered 30% of the current meteorological stations in Peninsular Malaysia. The cost analysis was based on the prices of mono-crystalline silicone solar PV in 2007. The study showed that PV system owners are not able to get any financial return on their investment in PV systems even after the governmentMalaya has provided subsidies of up to 70% (Seng et al., 2008).

In the present work, grid-connected solarof PV systems, have different capacities, will establish at 24 stations in Peninsular Malaysia, which is important to provide full information regarding the performance and up-to-date grid-connected solar PV electricity generation costs. In addition, viability of feed-in-tariff under government incentives will be highlighted.

2.4 Review of Renewable Energy Policies in Malaysia

The main purpose behind formulating any renewable energy policy is to ensure the long-termUniversity availability of environmentally friendly sources of energy. These sources should be reliable and cost-effective to mitigate the adverse effects from climate changes and achieve a low-carbon economy (Menz and Vachon, 2006).

Recently, Malaysia’s government has formulated many climate policies to cope with the unfavorable gas emissions challenges resulting from the rapid increase in energy demand. The target of these polices is to adopt an indicator of voluntary CO2 reduction

28 by up to 40% in terms of GDP emission intensity by the year 2020 compared to the

2005 levels. This can be done through mixing renewable energy in the electricity sector.

The fifth fuel policy in the 8th Malaysia Plan (2001–2005) was meant to contribute 5% of the country’s energy mix with Reusable Energy by 2005. Thus 70 million tons of

CO2 were supposed to be mitigated over a span of 20 years. In addition, to reduce dependency on the fossil-fuel mix energy sector, and to address the rising global concern on climate changes in an actively participative exercise. Unfortunately, this plan did not achieve its target (Hashim and Ho, 2011). Malaysia then followed up with the 9th Malaysia Plan (2005-2010) with a target of 5% renewable energy in the country’s energy mix. However, this target was not achieved, and the vast majority of

Malaysia’s energy generation still from the usual conventional sources of 65% gas, 28% coal, 5% large-scale hydro and 2% diesel (Oh et al.,Malaya 2010). After suffering for 8 years, and after analyzing the issues and intangible obstacles brought upon by previous failures, the Malaysian government launchedof the National Reusable Energy Policy in the 10th Malaysia Plan (2011-2015). The vision of the National Renewable Energy

Policy 2010 used the Feed-in tariff support mechanism to enhance the utilization of local renewable-energy resources to productively contribute towards the assured supply of national electricity for onward, sustainable socio-economic development (Malaysia

Plan, 2010; Hashim and Ho, 2011). In the present work, the feasibility of the Feed-in tariff support mechanism is highlighted and mentioned thereafter.

2.4.1University Incentives and Fiscal Support

In recent years, many studies have found that the actual design of support mechanisms will be in a position to guarantee maximum investment security that could reduce the cost of renewable energies by 10% to 30% (Jacobs and Sovacool 2012). For instance, if an investor is able to foresee the income revenue of a project, financial institutions will provide the capital at a lower cost, thus lowering the overall cost of renewable

29 electricity. Several types of support mechanisms have been developed, including:

Quota-Based Support (TGC and RPS); Net Metering; Feed-In Tariff; and Tax and

Investment Incentives. The advantages and disadvantages of these support mechanisms are critically discussed by Jacobs and Sovacool who came to the conclusion, by comparing many countries, that to create large scale gigawatt markets the only proven support mechanism as of now is the Feed-in Tariff (FIT) (Jacobs and Sovacool, 2012).

Malaya of

Figure 2.2 Renewable energy target in the 10th Malaysia Plan (Malaysia Plan, 2010)

In Malaysia, the Feed-in Tariff (FiT) was declared very late and following many deferments. In 2011, the Sustainable Energy Development Authority (SEDA) saw the launch of Malaysia’s FiT scheme to promote renewable sources coupled with the electricityUniversity energy sector. The target of the Feed-in Tariff (FiT) program was to increase renewable energy from less than 1% to over 5.5% and 985 MW as shown in Figure 2.2.

SEDA is a statutory body under the Ministry of Energy, Green Technology and Water

(KeTTHA) established under the SEDA Act 2011 (Act 726) (SEDA, 2011). Its purpose is to manage and administer the Feed-in Tariff (FiT) and the implementation of the

Renewable Energy Policy and Action Plan.

Table 2.1 Renewable Energy Tariff in Malaysia (Proposed for 2011)

Renewable Description Effective Malaysia Annual Resource of qualifying factor Period (RM/kWh) digression Solar PV Less than 4 kW 21 1.23 8% 4 kW-24 kW 21 1.20 8% 24 kW-72 KW 21 1.18 8% 72 kW-1 MW 21 1.14 8% 1 MW-10 MW 21 0.95 8% 10 MW-30 MW 21 0.85 8% Bonus for rooftop 21 0.26 8% Bonus for BIPV 21 0.25 8% Bonus for local modal 21 0.03 8% Bonus for local inverters 21 0.01 8% Biomass Less than 10 MW 16 0.31 0.50% 10 MW-20 MW 16 0.29 0.50% 20 MW-30 MW 16 0.27 0.50% Bonus for gasification 16 0.02 0.50% Bonus for steam generator 16 0.01 (14% more effic.) 0.50% Bonus for local manufacture 16 0.01 0.50% Bonus for municipal solid 16Malaya 0.10 waste 1.80% Biogas Less than 4 MW 16 0.32 0.50% 4 MW-10 MW of 16 0.30 0.50% 10 MW-30 MW 16 0.28 0.50% Bonus for gas engine (40% 16 0.02 more effic.) 0.50% Bonus for local manufacture 16 0.01 0.50% Bonus for landfill or sewage 16 0.08 gas 0.50% Mini-Hydro Less than 10 MW 21 0.24 0.00% 10 MW-30 MW 21 0.23 0.00%

The sources of funds for the Feed-in Tariff in Malaysia come from electricity consumersUniversity and are managed by Pusat Tenaga Malaysia (PTM). Consumers who utilize more electricity than a set minimum level must contribute 1% of their bill towards the fund (for every RM100/Month, an additional RM1/Month will be allocated to

Renewable Energy. The collected funds will then be used to equalize the price between the non-renewable and renewable sources of energy administered by SEDA (Hashim and Ho, 2011). Table 2.1 shows a FiT fund enforced since 2011. The FiT’s fixed rate

31 for solar PV power is RM1.23 to RM1.78 per kWh according to the type bonus, with an annual regression of 8% and displaced cost of RM 0.35/kWh under the Small

Renewable Energy Program (SREP) (Hashim and Ho, 2011). The companies that provide electricity to the national distribution grid system must first obtain approval from the Ministry of Energy, Green Technology and Water (MEGTW) under the Small

Renewable Energy Program (SREP). The maximum capacity that can be sold to the distribution grid system under this program is 10 MW (Green-tech-malaysia, 2013).

Another support mechanism in Malaysia is quota-based, under which the legislator obliges a certain market actor (consumer, producer or supplier) to provide a certain share of electricity from renewable energy sources (see Table 2.2). The choice of the obligated party (consumer, producer or supplier) usually depends on the national market design. The obligated party can either produce electricityMalaya itself or buy it from other green electricity producers in Malaysia (SEDA, 2013). However, businesses that use thermal energy -- normally derived from fossilof fuels, can benefit from fuel substitution to save on energy cost in the currently prevailing high fossil fuel price environment.

Commercial and industrial electricity users who wish to install grid-connected BIPV systems using renewable energy (RE) for power generation or for thermal energy use

(heating or cooling) for their own use can also apply for a license.

Table 2.2 Total FiT open from 2011 to 2014 under the quota-based mechanism (SEDA 2013)

Solar PV Solar Biogas Solid Small Less PV more Total Year Biogas Biomass UniversitySewage Waste hydro than 1 than 1 (MW) MW MW 2011-12 20 10 60 20 30 10 40 190 2013 20 10 50 30 30 10 40 190 H1 2014 10 5 25 15 45 5 20 125

As shown from table 2.4, SEDA has fixed a total quota-based mechanism of 511MW for various RE resources until the end June 2014 - 196MW for the year 2011/2012,

190MW for 2013 and 125MW for 1st half of 2014. The solar PV less than 1 MW

32 represents approximately 5% the quota-based mechanism, while the lion's share was for small hydro applications.

A different system of support entails bank fiscal incentives. This type of support has limited time and fund capacities. For example, in November 2011, Malayan Banking

Bhd (Maybank) and its Singapore unit, Maybank MEACP Pte Ltd, launched a US$500 million clean energy private equity fund to invest in a diversified portfolio of clean energy projects in the Asia-Pacific region, focusing on Thailand, China, India,

Indonesia, Malaysia, Vietnam, the Philippines, Cambodia and Laos. Emphasis is on clean energy projects in sectors that include geothermal, wind, solar, bio fuel, small hydro, biomass, and energy efficiency. Maybank Investment Bank reported that the primary investment deal was probably to be closed in July 2012. The fund would be offered globally, mainly to institutional investors, withMalaya a minimum investment amount of US$10 million. Maybank in Malaysia said it would invest US$50 million in the fund, while institutions such as the Asian Developmentof Bank (ADB) and International Finance Corp (IFC) would be the anchor investors. ADB would invest US$20 million and IFC US$25 million with the condition of its investment being lower than

US$25million or 20% of the total amount invested (EU-MALAYSIA BUSINESS

2013).

There are key issues raised by banks on the delay of approving applications for renewable energy. Banks admit there is a knowledge gap when it comes to evaluating greenUniversity technology projects. Further, there is a lack of a clear government structure and policies to support the green technology sector and as a result, this inhibits the banks’ ability to assess the full value and impact of projects. However, the delay in establishing the Feed-in tariff (FiT) and uncertainty with the returns on investments from the FiT provide little comfort to banks when assessing and approving loans. All these issues delay applicant approvals. For example, as of Dec 31st, 2011, only 36 applicants out of

118 received approval certificates issued by Green Technology (EU-MALAYSIA

BUSINESS, 2013). Hence, there is a need to evaluate green technology projects, a matter that stresses the purpose from the following work.

2.4.2 Renewable Energy Programs

In parallel with Malaysia’s renewable energy plan, several programs were launched during the 8th, 9th and 10th Malaysia plans in order to curtail the rising energy cost and threat of global warming. For example, Green Technology and Water (KeTTHA) was established in 2011 to manage and administer the Feed-in Tariff (FiT) and implement the Renewable Energy Policy and Action Plan. The small Renewable Energy Power

Programme (SREP) commenced on 11th May, 2001, to encourage private sectors to invest in small power generation projects using renewable energy (RE), such as biomass, biogas, mini-hydro, solar and wind energyMalaya (Hashim and Ho, 2011). The Biomass Generation Power and Demonstration (Bio-Gen) project was started in

October 2002 with the objective of promotingof and demonstrating biomass and biogas grid-connected power generation projects and reducing the growth rate of greenhouse gas (GHG) emissions from fossil fuel by utilizing excess oil palm biomass residues. The

Malaysia Building Integrated Photovoltaic Technology Application (MBIPV) operated for from 2005 to 2010 to reduce GHG emissions from the country’s electricity sector by reducing the long-term cost of the Building Integrated Photovoltaic mechanism. The

Centre for Education and Training in Renewable Energy and Energy Efficiency

(CETREE)University was initiated in August 2006 (Hashim and Ho, 2011). In this context, the present work will measure the success of informative promotional features, which is displayed in printed and electronic media by the above program to help people gain even a little knowledge about the government program.

2.5 Review of Current PV Technology for Power Supply

Renewable energy has a significant role in providing clean, cheap, reliable and alternative environmentally friendly energy sources for all our energy needs with zero or almost no resultant greenhouse gas emissions. The advanced technologies and innovations in the exploitation of renewable energy makes it possible to solve problems of local energy demand besides increase the standard of living and level of employment of the local population. Thus, sustainable development of remote regions in desert and mountainous areas is ensured; implementation of obligations of the countries with regard to fulfilling the international agreements on environmental protection (REN21,

2011). In addition, it has been reported that “Renewable energy will play a central role in the usage of the world’s future decarbonized energy, in which a study by EURELECTRIC suggests that 38% of the EuropeanMalaya Union’s electricity and approximately 50% of its generating capacity will be renewable by 2050” (Porter, 2012). The present work deals with solarof PV renewable energy and herein, the fundamental and current solar PV technologies are discussed. Cost criteria and modelling tools are also summarized in two subsections.

The first solar PV cell was built in 1839 by Becquerel, who discovered a device that could convert light straight into useful electricity. The conversion relies on the PV effect which, under the illumination of solar radiation, causes some voltage drop through the materials that have asymmetric electric resistance (Hirst, 2012). When the lightUniversity is incidental on some matter, it can supply sufficient energy to excite electrons at subatomic structure into higher energy states. In this case, the semiconductor material, such as silicon or germanium, allows an electron to escape from their bound condition and become free charge carriers moving towards the path with the least resistance.

Asymmetry in the material allows negatively charged free electrons to move to one side of the material, leaving the opposite side positively charged. As electrons accumulate at

35 one terminal, a potential that opposes the motion of the charged carriers is generated.

This potential creates voltage across the device. When the terminals of a solar cell are short-circuited, no charge will accumulate at the terminals as electrons will flow inconsiderately across the short circuit to the opposite terminal. In this instance, maximum current will flow but no voltage will be generated. When a large load of resistance is placed across the terminals, a large number of electrons will collect at the terminals, producing a large voltage across the device but limiting the current flow.

Today, there are four types of PV technology available on the market. The first is crystalline silicon (c-Si) modules, which are subdivided into two main categories: single crystalline (sc-Si) and multi-crystalline representing 85% to 90% of the global annual market. The second is a thin film, subdivided into three main families: amorphous (a-Si) and micro morph silicon (a-Si/μc-Si), Copper-Indium-DiselenideMalaya (CIS) and Copper- Indium-Gallium-Diselenide (CIGS), and Cadmium-Telluride (CdTe), representing 10% to 15% of the global annual market. The thirdof type includes new emerging technologies such as advanced organic and thin films. Such technology enters the market via a niche application representing less than 1% of the global annual market. The fourth PV technology type is the Concentrator Photovoltaic technology (CPV), whereby optical concentrator technology is used in order to focus the solar radiation onto a small, high efficiency cell. This technology is currently being tested in a pilot application and represents less than 1% of the global annual market (International Energy Agency

2010).University Figure 2.3 provides information regarding the market and performance of different PV technologies.

Figure 2.3 Global marketing of different PV technologies (source: IEA, 2010)

Two types of photovoltaic (PV) applications are available,Malaya namely the grid-connected solar PV and the stand-alone solar PV. Theof grid-connected PV systems consist of one or more PV modules with one or several inverters to convert the PV direct current (DC) into alternating current (AC) with cabling connected to the conventional electricity grid via the inverter. The size of the grid-connected PV system ranges from approximately small scale for private homeowners (0.5 to 4 kWp), to medium scale (4 to 100 kWp), to large scale (0.1 to 100 MWp), while very large-scale PV (VLS to PV) systems may be above 1 GWp. Additional components of the modules that together form a PV system areUniversity denoted as balance-of-system (BOS) components (van, 2012). The BOS represents components that are used to balance the structures for mounting the PV modules and power-conditioning equipment that convert the generated electricity for usage in the power grid (Fthenakis and Kim, 2012).

Figure 2.4 The progressive development of solar PV efficiency (Source: NREL)

Globally, solar PV technologies have witnessed progress in last few years, recording an annual production of more than 20 GW in 2010 andMalaya business of approximately $70 billion at the beginning of 2011. In addition, production growth exceeded 30% annually over the last 12 years, which will reflectof a decrease in solar PV prices (Kazmerski,

2012). However, the recorded efficiency of solar cells on the market increased as well to reach 22.4% as recorded by SunPowerTM solar cells (SUNPOWER, 2012). In addition, it is expected that solar cell efficiency will increase based on the active development of multi-junction materials. The greatest solar cell efficiency recorded by multi-junction solar cells had a value of 40% (NREL, 2011). Figure 2.4 demonstrates the progress in PV cell efficiency based on the best research as reported by the National

RenewableUniversity Energy Laboratory (NREL).

2.5.1 Clean Energy Cost Criteria

In the real world, energy options cannot be selected merely based on technical feasibility; the economic aspect is also a crucial factor. Therefore, even if the system’s efficiency and energy productivity are accurately quantified, there is still a question to be rationally answered as to the final cost of the energy produced. Hence, any energy

38 producing project should reach optimum cost per energy generated (kWh) so as to be viable.

Several factors can affect the unit cost of electricity produced by a renewable energy system, making the economic issue of this technology a multi-dimensional problem. For example, the energy produced by a solar PV system heavily depends on the local weather parameters as well as metrological conditions, rendering PV power different from location to location even in the same country. Another stinging issue is the high capital investment in this technology and the various market options and uncertainties in the key parameters. Hence, it is necessary to consider these issues in planning for investment in renewable energy rationally and sensibly without any ambiguity.

Three economic criteria to determine the feasibility of renewable energy systems and that have been used in cost analysis are NPV, IRRMalaya and LCOE. The net present value (NPV) is the summation of the total present value of a time series of cash flow through the project lifespan, which includes the initialof capital cost, operation and maintenance cost, and replacement cost of major components of the installation system. Finally, the residual value at the end of the project lifespan should be subtracted from the summation (Hanley et al., 1993; Deshmukh and Deshmukh, 2008). The internal rate of return (IRR) is the discount rate that dictates the NPV being made to equal zero during any given time period. However, it is frequently used by corporations to compare and decide among capital projects (Bakos and Soursos, 2002; Kavadias, 2012). The levelisedUniversity cost of energy (LCOE) is used to evaluate the cost of energy delivered from different power generating technologies, specifically to grade options and select the most cost-effective energy source as shown in Figure 2.5.

Figure 2.5 Summary of the average LCOE values for different technologies used in power plants entering service in 2016 in the US (Tarn and Bradley, 2013)

If meticulously analyzed, the LCOE can be quite effective and thus considerably assist policy makers to decide on how future energy needsMalaya will be managed and which technologies to be promoted or supported for utility purposes. For example, as the LCOE includes total operating costs, andof it can also facilitate making comparisons between significantly different technologies, wherein it can be used to determine the performance or cost benchmarks for the express purpose of being competitive on the market. Figure 2.5 shows how LCOE has been used to identify the cost of different power plants in the USA; it is clear that the natural gas is the cheapest among traditional resources. However, the hydro and geothermal energies are the cheapest among all types of renewable energy resources in the USA, whereas the LCOE of the solar PV systemUniversity is $210/MWh or $0.21/kWh (Tarn and Bradley, 2013).

One of the major factors limiting the wide-spread acceptance of solar PV technologies is their high generation cost. Nevertheless, the speedy progress in the application of renewable energy coupled with its rapid growth plus market competition and increasing supply and demand will ultimately make the solar energy production business competitive with other sources like natural gas or coal with the associated cost

40 reduction incentives. A study by the U.S. Energy Department reported that installed prices of residential and commercial PV systems had declined by 5% to 7% per year on average from 1998 to 2011, and by 11% to 14% from 2010 to 2011, depending on system size (Feldman et al., 2012). This also reflects the fact that even data few-years old can significantly affect the estimated energy cost of renewable energy resources at a later, nearby period. In the present work, the analysis was based on the above principles of engineering economics and the mentioned criteria for assessing the financial merit of renewable energy projects. However, the ‘sensitivity analyses and the present worth’ approach will be adopted in our discussion. A definition of utilizing the main parameters and a presentation of the models will be provided in the methodology chapter. The following section examines the major considerations and tools employed in such projects. Malaya 2.5.2 Clean Energy Evaluation Tool

Several research works ongoing to createof viable and feasible future investments in renewable energy technology so as to foster its intensive global participation for the benefits of the global community in the foreseeable near future. A wide range of electronic software is also being developed for analytical purposes regarding the energy produced and the proven integration of this technology with different energy systems.

Connolly et al. ranked according to usage these software tools, which have been used to optimize and design renewable energy systems (Connolly et al., 2010). In this thesis, theUniversity focus is only on HOMER as it is currently used extensively in many research papers and ranks second on Connolly's list.

The Hybrid Optimization Model for Electric Renewables (HOMER) was developed by the National Renewable Energy Laboratory of the US and can be used to analyze micro power system projects as well as perform comparisons of power generation among different technologies in various applications (Georgilakis, 2005; Lambert et al., 2006;

HOMER Energy, 2012). With sophisticated component applicability, the software is capable of modeling a power system and estimating its behavior and total cost through the project lifespan.

After the data input procedure is completed, the model will appropriately simulate the operational system by executing energy balance estimations on an hourly basis for one year. Based on each hour's calculation, the electricity load will be compared with the energy produced by the system, after which HOMER decides how to operate the generator and weather to initiate electrical charge or discharge on the storage medium.

During the process of energy estimation, if the designed system is to cover the load for the entire year, HOMER will estimate the lifecycle cost of all the systems involved, such as accounting for capital cost, operation and maintenance costs, interest cost and eventual salvage value. The principal layered tasks Malayaof the HOMER program are shown in Figure 2.6. Further details can be found ofat http://homerenergy.com/ .

University Figure 2.6 Principal layered tasks of the HOMER program

The main advantage of HOMER is the optimization of system components to determine the best possible system configurations. Ultimately, a final analysis is done to reveal the output’s sensitivity to the variation in input parameters (Lambert et al., 2006). However, the main disadvantage of HOMER is that it is mainly used to design an economic model

42 dedicated to system selection and pre-sizing, and it cannot be used for system design requirements.

A number of published research papers from different countries that have used

HOMER. In Malaysia, HOMER has been used to perform feasibility analyses of several different stand-alone poles for street lighting (SPSL) using light-emitting diode (LED) lamps on Penang Island in northern Malaysia. The analyses indicate that using renewable energy-based solar radiation to power street lighting is not recommended in locations with an electrical grid in Malaysia (Wadi, et al. 2014). Dalton et al. carried out a feasibility analysis on the ability of a renewable energy system (RES) to supply power to a large stand-alone hotel with over 100 rooms, located in a subtropical coastal area in

Queensland, Australia. The study showed that the RES configuration could fully supply the power demand of the hotel (Dalton et al., 2008).Malaya In Iran, Askari and Ameri conducted a feasibility analysis which examined several RES options for supplying power to 50 rural houses in the remoteof area of Kerman, Iran. Different RES configurations were assessed, namely PV, wind turbine generators, and an integrated combination of both PV and wind generation. The paper suggested that the PV configuration is the most economically efficient choice (Askari and Ameri, 2012). In

Newfoundland, Canada, HOMER has been used for sizing and the optimization of tools for a hybrid system with hydrogen as an energy supply carrier for stand-alone houses in remote areas. Sensitivity analysis has also been performed using HOMER on wind speedUniversity data, solar radiation levels, diesel prices and fuel cell cost. This study found that a wind–diesel–battery hybrid system seemed to be the most suitable solution at the moment. However, with a reduction in fuel cell cost of up to 15% of its current value, a wind–fuel cell system may alluringly become a superior choice (Khan and Iqbal, 2005).

In the present work, an analysis has been done using HOMER to reveal the output’s sensitivity to the variation in input parameters as additional tools for analytical

43 performance. The procedures of estimation and equations in the methodology chapter are same that are used by HOMER except equation (3-7), which considered the impact of the velocity parameter in simulation. This approach is to compare the results with

HOMER at the end of simulation.

2.6 Social Acceptance Review

In the field of energy policies, the concept of social acceptance refers to the public response to adopting a specific technology. Several scholars worldwide agree that implementing renewable energy technologies and achieving a government energy policy is not feasible without social acceptance (Mallett, 2007, Wüstenhagen et al.,

2007). However, Wüstenhagen et al. (2007) pointed out that it is hard to achieve the goal of renewable energy development with low levels of social acceptance; therefore it should be taken during the policy making. He developedMalaya a three-dimensional model for renewable energy social acceptance, including community acceptance, market acceptance and political acceptance. Delof Río et al. (2009) mentioned that local residents’ social acceptance of renewable energy is influential not only to the renewable energy project itself, but also to the success of sustainable development in that region.

Recently, many studies have shown different indicators for measuring social acceptance in a particular context. Among these are the participants, socio-economic background, age group, political beliefs, facilitating conditions and the perceived usefulness (Munjur et University al. 2013). For instance, Zoellner et al., (2008) studied the public acceptance of renewable energies (wind energy, biomass plants and large-scale ground-installed grid- connected PV systems) in Germany. The survey employed about 394 participants. The qualitative data analyzed demonstrated the relevance of the operating company's commitment on the local level through the participation of the general public. In addition, the plant location choice was among the more relevant aspects for acceptance in the implementation process.

In China, Yan et al. examined the social acceptance of solar energy technologies. A total of 1271 questionnaires were distributed and returned. The results indicate considerably high levels of social acceptance and public awareness regarding solar water heaters. Though, another major application of solar energy, such as solar PV energy, has not gained a high level of social acceptance and awareness in Shandong

(Yuan et al., 2011).

In Finland, Moula et al. investigated the social acceptance of renewable energy technologies. The survey included 50 participants and 14 multiple choice questions covered various issues in two categories. The first group was the participants’ background and the second regarded the people’s willingness to invest in renewable energy. According to the results, 62% of the people were willing to pay extra to obtain green energy. However, more than 52.4% of the peopleMalaya thought that the public sector should take the first step toward renewableof energy production (Munjur et al. 2013). In the present work, social acceptance is deemed an important subject that may help with the extensive implementation of renewable energy technologies in Malaysia. A survey was developed to analyze the people’s level of awareness regarding the adoption of solar PV energy through government incentives. It will be shown how Malaysian people regard general issues such as global warming and renewable energy, as well as their willingness to pay for clean energy, and their acceptance of renewable energy. In thisUniversity context, the results of the developed study will provide a treasure trove to strengthen any future policies and formulate regulatory frameworks to encourage the widespread adoption of renewable energy projects.

2.7 Summary

 The total primary energy supply has increased steadily over the past 12 years in

Malaysia; it was estimated to reach about 83.9 MTOE by 2012, which is more

than a 290% increase from 1990. Apart from that, the final fuel consumption

rose at an annual growth rate of 6.8% from 1990 to 2012 and reached 46.7

MTOE in 2012 with the anticipated resultant consequence of unavoidable

depletion of natural sources.

 It is to be noted with concern that the total carbon emissions from Malaysia in

2006 were 171 million tons, which rose to approximately 217 million tons in

2010 -- an incredible increase of 46 million tons in a short span of just 4 years

and a huge contribution of greenhouse gas emissions into the global atmosphere. Malaya  Malaysia ratified the Kyoto Protocol on September 4th, 2002, to control and reduce greenhouse emissions. The ofgrand vision of being a developed country by 2020 reflects that the increase in energy demand and greenhouse emissions are

currently two challenges potentially facing the government of Malaysia.

 To cope with the national electricity supply and reduce greenhouse gas

emissions, the Malaysian government has adamantly launched a national

renewable energy policy through the 10th Malaysia Plan (2010-2015), which is

expected to have a positive impact by 2020. University Solar radiation in Malaysia remains unclear and there are no reliable solar radiation maps. Empirical models for solar radiation plants have been

developed for specific locations and are limited in scope and application. These

models have severely failed to take into account the impact of the complex

terrains and mountain ranges, making it difficult to extend these models’

prediction and leading to significant errors.

 The artificial neural network (ANN) is good at fitting functions and

reorganizing patterns. It has also been trained to solve problems that are

difficult for humans to perform even with conventional computers. Therefore,

ANN can be used for solar radiation prediction as it is capable of considering

the impact of the complex terrains and mountain ranges in the assessment of

solar radiation.

 There is a chronic shortage of solar energy implementation in Malaysia mostly

because of the inaccurate or sometimes misleading perceived knowledge about

solar energy resources as well as the cost and performance of photovoltaic solar

power generation. For example, Banks admits there is a knowledge gap when it

comes to evaluating green technology projects, which reflects a delay in application approvals. This reveals the needMalaya to assess several renewable energy projects for sustainable development.  It is hard to achieve the goal of renewableof energy development with low levels of public acceptance. Social acceptance is something that can help with the

extensive implementation of renewable energy technologies in Malaysia.

Hence, a survey was developed to measure and analyze people’s level of

awareness regarding the adoption of solar PV energy through government

incentives. University

CHAPTER 3: METHODOLOGY

3.1 Introduction

The methodology section mainly comprises five sections, the first of which is the introduction. The second presents the modelling of solar radiation using Artificial

Neural Networks to describe the complex relationships between solar radiation and other meteorological parameters. In this context, 12 models are developed, after which the designed model with the most accurate solar radiation prediction can be fruitfully used to predict new data at distant locations. The predicted database can then be used to effectively develop adequate solar radiation maps using ArcGIS.

Sections 3 and 4 include the technical and financial analysis of two relevant applications that use solar resources for electricity production. One is supported by the government, and that is the grid-connected solar photovoltaicMalaya (PV) application. In this context, two models are prepared for energyof analysis: Model-1 and Model-2. The first model works without the impact of wind speed, while the second with the impact of wind speed. The most important parameters affecting the cost of energy production are highlighted as well, to be separately discussed during sensitivity analysis.

The second application studied is the feasibility of solar radiation powering hybrid standalone street lights in a remote area. In this context, Model-1 and Model-2 are prepared using HOMER analysis tool: the first is a normal model and the second is an energy-savingUniversity model. The purpose of these kinds of application is to study applicability to create investment areas and develop a new policy according to the new findings.

Section 5 shows the methodology of the survey conducted to investigate social acceptance regarding solar energy applications and what people know about the Feed- in-Tariff (FiT) incentives. In same context, the obstacles making people unwilling to

48 accept the government’s incentives are investigated, which place Malaysia in a chronic shortage situation regarding solar energy application.

3.2 Modeling Solar Radiation Using Artificial Neural Networking

Sun power is immense, and easy to access and harvest. Acquiring and compiling solar radiation resources is a central point for a thoughtful and durable approach of energy development (Ranchin et al., 2002). In this sense, there is a need for modelling solar energy as a fuel source for applications of solar energy conversion systems. This still represents one-half of the equation, while the second half identifies locations appropriate for solar energy development based on confirmed information on the quantities and places where such energy is available. In fact, such information is critical to beneficiaries such as designers, investors, financiers, and home owners, who are the ones willing to make any investments in the field ofMalaya alternative energy (Myers, 2012). As such, solar radiation assessment is divided into two subsections. The first section deals with modelling solar radiation, whereof the developed model should be accurate enough to extrapolate solar radiation data from a meteorological station to a new area.

The second step entails using ArcGis9.3 to develop solar contour mapping over

Peninsular Malaysia to visualize the locations of solar resources.

3.2.1 Location & Data Utilized

The land mass of Malaysia is divided into two large regions, namely the Eastern and WesternUniversity Coasts, which cover a total area of 328,600km2. Peninsular Malaysia covers 131,600 km2 while Sabah is around 73,700 km2 and Sarawak is roughly 123,000km2.

Peninsular Malaysia covers 39% of the total land area and consists of nine big states extending from 1o20’ to 6o40’ latitude north and 99o35’ to 103o20’ longitude east.

Sabah and Sarawak are called East Malaysia and are separated from Peninsular

Malaysia by 720km of sea. Topographically, Peninsular Malaysia is characterized by extensive coastal plains in the east and west. Hilly and mountainous regions with steep

49 slopes are in the central region while the undulating topographical features form the rest. Since Malaysia is in the tropics, it is affected by south-western and north-eastern monsoons, of which the south-western monsoon brings in more clouds and rain from

June to early October. The north-eastern monsoon precipitates torrential downpours from heavy clouds from November to March with potential heavy flooding in several low lying areas. The climate in Malaysia is typical of the humid tropics and is characterized by year-round high temperatures and seasonal heavy rains. The temperature ranges from 26oC to 32oC and the rainfall ranges from 2000 mm to 4000 mm per annum. Rainfall is torrential due to the influence of both the north-eastern and south-western monsoons (RRC.AP., 2013). Malaya of

University

Figure 3.1 Geographical locations of the meteorological stations in Peninsular Malaysia.

In Peninsular Malaysia there are very few meteorological stations harvesting global solar radiation (Figure 3.1). Only 24 ground stations cover the complex topography of

Peninsular Malaysia. In these stations, the hourly data was recorded using a

Pyranometer, which is a type of measuring device used to measure combined direct and diffuse solar radiation based on the absorption of heat by a black body. They are operating under the Meteorological Station Department of Malaysia, but the majority of

Stations are currently not working. Hence, inaccurate and inadequate record data on solar radiation is a major challenge facing engineers and researchers in the field of renewable energy. Table 3.1 shows the geographical coordinates of these stations.

Table 3.1 Geographical locations of the meteorological stations in Peninsular Malaysia

Latitude Longitude Height Code Station Name (E) (N) (MSL) Recording Period 41529 Pulau Langkawi 6° 20' 99° 44' 6.4 July 1987 - 2010 48600 Bayan Lepas 5° 18' 100° 16' Malaya2.46 1975 - 2010 48603 Alor Setar* 6° 12' 100° 24' 3.9 Feb. 1980 - March 2008 48604 Chuping 6° 29' of100° 16' 21.7 1979 - March 2007 48615 Kota Bharu 6° 10' 102° 18' 4.36 Feb. 1975 - July 2009

48616 Kuala Krai* 5° 32' 102° 12' 68.3 Aug. 1986 - 2008

48618 Kuala Terengganu Airport 5° 23' 103° 06' 5.2 March 1985 - 2008

48620 Sitiawan 4° 13' 100° 42' 6.78 March 1976 - May 2008

48623 Lubok Merbau 4° 48' 100° 54' 77.5 Feb. 1993 - Nov. 2006

48625 Ipoh 4° 34' 101° 06' 40.1 1976 - 2010

48632 Cameron Highlands 4° 28' 101° 22' 1545 1985 - Sept. 2008 48642University BatuEmbun 3° 58' 102° 21' 59.5 Sept. 1983 - March 2007 48647 Subang 3° 08' 101° 33' 16.64 1975 - 2010

48648 Petaling Jaya* 3° 06' 101° 39' 60.8 1977 - 1999

48649 Muadzam Shah 3° 03' 103° 05' 33.3 Aug. 1983 - 2010

48650 KLIA Sepang 2° 44' 101° 42' 16.11 July 1998 - 2010

48653 Temerloh* 3° 28' 102° 23' 39.1 Aug. 1978 - Aug. 2006

Table 3.1 ‘Continued’

48657 Kuantan 3° 46' 103° 13' 15.23 1980 - 2010

48665 Malacca 2° 16' 102° 15' 8.5 Sept. 1976 - March 2010

48670 BatuPahat 1° 52' 102° 59' 6.3 1993 - Jan. 2006

48672 Kluang* 2° 01' 103° 19' 88.1 May 1977 - Aug. 2006

48674 Mersing 2° 27' 103° 50' 43.6 July 1976 - Aug. 2008

48679 Senai 1° 38' 103° 40' 37.8 Apr. 1985 - July 2010

48619 KajiklimKuala Terengganu 5° 20' 103° 08' 35.1 Sept. 1976 - 1984

* denotes stations used for testing in ANN, and no star means training stations

3.2.2 Modelling Solar Radiation

Since Peninsular Malaysia has a harsh and adverse topography, any prediction method should always take the topography and elevation aspects into consideration, as these are important variables since they have a large impact on solar radiation distribution. The peculiar structure of the variability factor in a regionMalaya may be due to the physical attributes of that particular area. For instance,of the mountain range in Peninsular Malaysia may cause clouds to form preferentially aligned with the mountain ridges or there may be a land/sea boundary. The most important point is to check how and why this newly re-mapped solar irradiation agrees with the topographical features and localized cloud patterns that in turn agree quite well with the monthly average solar radiation. In order to surmount the topographic challenges, the literature review indicates that Artificial Neural Network is potentially the best option for solar radiation modelling,University as it can deal with non-linear parameter radiation modelling that will be mentioned later.

3.2.3 Artificial Neural Networking (ANN)

A neural network is composed of simple elements which, incidentally, operate in parallel. The idea of these elements derived from studies of our own biological nervous systems. The connections between these elements superbly decide the network function

52 through the continuous comparison of weights between the elements in terms of adjustments through ANN inputs and outputs. Thus, continuous network adjustments will be made until the network output matches the target, as several input and output pairs are needed to train the network. Several different research works and articles have shown that ANN is good at fitting functions and reorganizing patterns, and has also been trained to solve problems that are difficult for humans to perform even by using conventional computers. Hence, it has been used in many analytically and scientifically reported studies by DARPA (Hagan et al., 1996).

The ANN uses simple processing units called neurons, combines data, and stores relationships between the independent and dependent variables. An ANN consists of several layers, each of which has many interconnected neurons. There are two types of ANN: single-layer (SL) and Multi-Layer PerceptronMalaya (MLP).The latter is widely used in neural network (NN) models. It comprises three parts connected together. The first part represents the input layer, the middle part ofwith one or more layers is called the hidden layer, and the last part represents the output layer. There are several neurons (nodes) inside each layer, and each node in every layer is connected to the nodes in the adjacent layer with different weights. These weights have no discriminative characteristics in the initial stage, but after receiving several trainings, they can contain meaningful information. Signals go through these layers in the first pass through the input layer, after which the signals pass through the hidden layers before passing through the output layers.University

In this process, each neuron receives signals from the adjacent layer in the previous layer (Xij), as shown in Figure 3.2. However, the signal will come with its weight (Wij) and will then be summed up with the bias (bj) contribution. This can be expressed mathematically as:

n netXj  V b j ijj (3-1) i 1

Figure 3.3 ANN dependency graph, including the input, summation function, transfer function and output.

The output of neurons is then calculated by applying an activation function, which is given in Table 3.2 as the total input computed by Equation (3-1) as shown by the transferUniversity function location in Figure 3.3. In the first iteration, if the computed outputs do not match the known target values, it means that the NN has an error, in which case, a portion of this error will be returned as feedback through the network to adjust the weight and bias of each neuron again. For the next iteration, this error will be less than before. This procedure will continuously repeat for each input set until there is no

54 quantifiable error or the permissible error tolerance limit is reached, which is smaller than the specified value shown in Figure 3.4.

Figure 3.4 Feedback error that adjusts the weight between neurons.

Table 3.2 Type of activation functions in the artificial neural network.

Function Definition Range

Linear (PURELIN) 푥 −∞ < 푥 < ∞ 1 Malaya Logistic (LOGSIG) 0 ≤ 푥 ≤ +1 1 − 푒−푥 푒푥 − 푒−푥 Hyperbolic (TANSIG) of −1 ≤ 푥 ≤ +1 푒푥 + 푒−푥

3.2.4 Design of the Artificial Neural Networks Model

In the present work, a multi-layer feed-forward and back propagation network is used, which is typical of a hierarchical network as shown in Figure 3.5. Twelve different architectures were designed in order to get the best model with acceptable accuracy.

MATLAB version R2013a with built-in ‘Neural Network Toolbox’ was used as an analyticalUniversity tool. Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms together were used in the network. The developed model involves three main layers: the input, hidden and output layers. There are five input parameters for the network’s input layer, which are the geographical coordinates (latitude, longitude, and altitude), extraterrestrial solar radiation and the months through the year.

In the hidden layer, a different single layer and double layers were used. These layers

55 have different neurons which vary from 1, 2 to 50 intervals, to enhance the ANN’s searching performance capability. In the output layer, there is one output corresponding to global solar radiation. The output of neurons is calculated by applying an activation function, such as the Hyperbolic (TANSIG) transfer function, which is used in the hidden layer while the linear transfer function is used in the output layer. It is appropriate to mention that if the sigmoid transfer function is used in the output layer, as in such a case, while the ANN is used in pattern recognition problems, it means the decision is being made by the network. Before executing the training process, it is important to normalize the input and output values to a range of +1 to -1 to make training faster where the weight estimation can be done more conveniently with standardized inputs. There are a total of 12 developed networks, out of which the best design is allocated for use in solar radiation prediction.Malaya There are 24 solar radiation stations in Peninsular Malaysia, and in the present work, 19 stations are used for training the ANN by adjusting the weightsof and biases of neurons; the remaining 5 stations are used for testing the predicted data. The output has been designed to automatically avoid ‘over fitting’ of data, and the ‘early stopping’ technique has also been used in conjunction with algorithm training in cases of error from computations become augmented.

University

Figure 3.5 Typical neural network for global radiation prediction.

Last but not least, the correlation ships between R, RMSE, and MAPE are used to evaluate the results of training, validation and testing during the training and testing of the logarithm process to optimize the prediction capacity.

3.2.5 Accuracy of Solar Radiation Predictions

Solar radiation prediction is considered accurate if it is performed according to the quality of measured data, which is normally defined by three statistical error formulas.

Firstly, the Mean Absolute Percentage Error (MAPE), which is sometimes known as the

Mean Absolute Percentage Deviation (MAPD), represents the difference between the predicted and real values. In this context, based on the mean absolute percentage error

(MAPE), if the MAPE value is more than 50%, this means inaccurate forecasting. If

MAPE lies between 50% and 20%, this means reasonable prediction. If MAPE is between 20% and 10%, it represents good prediction,Malaya and the best prediction is only achieved when MAPE is less than 10% (Yadav and Chandel, 2013). Equation 3-2, o is the output value, t represents the target value,of and j represents the number of iterations.

otij MAPE 100 (3-2) oi Secondly, the RMSE provides information on the variation of predicted values around the measured values during any short-term performance (Equation 3-3). In this equation, p is the pattern.

1 2 1 2 RMSE t o (3-3) p  j j Universityj

Thirdly, the Correlation Coefficient Factor denoted by R is an indication of the relationship between the output and targets. However, the Correlation Coefficient R, also known as the Pearson product-moment, is important; it is used in mathematics, science and more significantly, statistics, as a measure of the strength of the linear relationship between two variables. Where, if R=1 there is exactly a linear relationship

57 between the ANN output and the target, while if R=0, there is no relationship between the output and the target. The statistical error formulas are also given in the following equations respectively.

2 to  jj j (3-4) R1 2 o  j j

These errors above are similar to those in the ANN learning process, and are computed from the global statistic equations (3-2), (3-3) and (3-4).

3.2.6 Mapping Solar Radiation

After the developed artificial neural networks were trained with sufficient precision, the best model was used to predict the solar radiation in 250 cities in Peninsular Malaysia. The geographical descriptions of these cities wereMalaya obtained from the Meteorological Department of Malaysia and are presented in Appendix A. These also coincide with the yearly predicted solar radiation. The geographicalof information system software ArcGIS version 9.2 was used to visualize the solar potential from January to December through the solar maps. To increase the accuracy of the solar radiation maps, the ANN was used to extend the predicted monthly mean solar radiation to 292 extra locations. These extra points were obtained from the digital Malaysian topographic raster map.

3.3 Modeling Solar PV of Grid-Connected Applications A University feasibility study is an evaluation and analysis of the proposed project’s potential, which should normally be based on extensive investigation. The purpose of such study is to support the process of decision making by covering the strengths and weakness of an existing business or proposed venture, and ultimately, the prospects for success

(Justis and Kriegsman, 1979; Georgilakis, 2005). There are two criteria for judging feasibility: the cost required to establish the project and the time to recover the invested money (Young, 1970). In our case, feasibility means the expected benefit if people in

Malaysia decide to invest in grid-connected solar PV applications according to the feed- in-tariff mechanism. The following section presents the step-by-step modelling and assumptions used in feasibility analysis.

3.3.1 Photovoltaic System Modelling

Estimated energy produced by solar PV systems is the first step in feasibility analysis.

In the present work, two models were prepared: Model-1 and Model-2. The first model works without considering the impact of wind speed (In ordered to compare the results with HOMER), while the second model takes the impact of wind speed into consideration. Energy handled by the PV panel is linearly proportional to the incident radiation. Hence, the energy produced by PV is given by the following equation

(Fantidis et al., 2013).

G P Y f f T Malaya (3-5) PV PV PV c  GT, STC Where YPV represents the peak power (kWp)of produced from PV, which are typically o 2 rated at standard test conditions (STC) of 25 C cell temperature (Tc,STC), 1000 W/m irradiance (GT,STC). Factor fPV is the PV derating factor in percentage, which is the measured difference between powers produced by the solar array in the lab to that produced by the same array in the field. The difference comes from many factors, such as soiled panels, wiring losses, shading, snow and other weather conditions. Therefore, an experimental study was developed at the UM Power Energy Dedicated Advance CentreUniversity (UMPEDAC) building, to investigate the most proper derating factor value. The system comprises a 1 kWp photovoltaic array system type PV-AE125MF5N developed by Mitsubishi Electric Company, connected in series to a supply of 215 open circuit voltage. The technical specifications of solar panel are as shown in Table 3.3.

Table 3.3 Technical specifications of solar panel of grid-connected solar PV system Cell Type Polycrystalline Cell

STC Power Rating Pmp (W) 125

Open Circuit Voltage Voc (V) 21.8

Short Circuit Current Isc (A) 7.9

Voltage at Maximum Power Vmp (V) 17.3

Current at Maximum Power Imp (A) 7.23

Panel Efficiency 12.40%

Fill Factor 72.60%

Power Tolerance -5.00% ~ 10.00%

Maximum System Voltage Vmax (V) 1000

Maximum Series Fuse Rating (A) 15

Temperature Coefficiency of P Malaya-0.45 %/ºC mp Cell Size(mm) of 156 × 156 Cells 4 × 9 1495.0 × 674.0 × Dimensions 46.0mm Weight 13.5Kg

Nominal Operating Cell Temperature (NOCT) 47.5ºC

Operating Temperature -40.0ºC to 85.0ºC

Maximum Load 2400 90%University Power Output Warranty Period 10.0 Years 80% Power Output Warranty Period 25.0 Years

However, the PV system is connected to a DC-AC inverter model SPI2000-B. The output of the inverter is fed to the global outside electric-powered grid using an electric meter in between to record the energy produced (Figure 3.6).

Figure 3.6 Grid-connected PV system and its components (inside energy analysis room)

Factor fc in equation (3-5) refers to the reduction in PV power resulting from the increase in PV cell temperature, which indicates how strongly the PV array power output depends on the cell temperature (the PV array’s surface temperature). It is defined by equation (3-6): Malaya

f1  T  T (3-6) c p c c, STC  of Factor  is the temperature coefficient of the power (%/oC), which is normally p negative, meaning that the efficiency of the PV array decreases with increasing cell

o temperature. Tc,STC represents the cell temperature under standard test condition [25 C].

o The Tc( C) is the temperature of the PV array surface in the current time step.

During the night, it is approximately same than the ambient temperature, but in full sun theUniversity cell’s temperature can exceed the ambient temperature by 30oC or more, causing a decrease in overall PV efficiency. The cell temperature is given by the following equation (Duffie and Beckman, 2013):

G STC1 PT c, STC  TTT  T 1 f a c,, NOCT a NOCT v GT, NOCT  T   c  GT STC P 1TTc,, NOCT a NOCT fv G  T, NOCT (3-7)

The temperature in the above equation should be in kelvin. Factor  represents the solar absorbance of the PV array and factor  is the solar transmittance of any cover over the PV array. Tc,NOCT is the nominal operating cell temperature reported by the factory, which is defined as the cell temperature at incident radiation G of 0.8 T, NOCT

2 o kW/m , ambient temperature Ta,NOCT of 20 C, and no load operation (cell = 0). Factor fv is called the velocity factor, the value of which is given by the following equation

(Reindl, Beckman et al. 1990):

9.5 f  (3-8) v (5.7+3.8V) where V represents the average velocity. It should be mentioned that in some clean energy management software such as RETScreen or HOMER, the variation of velocity through time is not considered, therefore, it is assumedMalaya that the velocity is constant at all times and takes a value of 1 m/s, meaning thatof fv is equal to 1. Finally, Photovoltaic power capacity factor (CF) is measured as maximum power output from the system to the rated power of that system under standard test conditions (STC) in "Wp." In the grid-connected solar PV system, the actual power output at a particular point in time may be less than or greater than this standardized, or "rated," value.

3.3.2 Solar Radiation Modelling

The solar radiation ( GT ) incidents on the earth change from time to time. Thus, many modelsUniversity and definitions have been developed to estimate the solar radiation incident on horizontal and inclined surfaces, which is important for different purposes such as measuring the output power from solar PV or cooling/heating load calculations for buildings. In the present work, it is necessary to define some important parameters related to estimating total solar radiation incident on solar PV. These variables are

62 extraterrestrial radiation, clearness index, direct and diffuse radiation, which will remember in sequence.

3.3.2 (a) Extraterrestrial Radiation

Extraterrestrial solar radiation Go may be defined as the solar radiation incident on a horizontal plane outside the atmosphere for any point of time. This is the same sunlight reaching the horizontal surface on the ground, regardless the effect of the atmosphere.

The amount of extraterrestrial radiation reaching the horizontal plane depends on the distance between the earth and the sun through the earth’s elliptical orbit around the sun and the earth’s axis declination through that trajectory. The daily extraterrestrial solar radiation on a horizontal surface in joules per squared meters for a one-hour period is given by the following equation (Reindl et al., 1990). Malaya    12 3600  360n  21  (3-9) GG0  sc 1 0.033 cos cos  cos    sin  2  1   sin  sin     365 180 2 Where Gsc is the solar constant (1367W/mof) and n represents any day in the year of 365 days for daily estimations. For monthly estimation, it will be the day in the month opposite the average declination in that month; variable  is the earth’s latitude; variable represents the declination of the earth’s axis through a day of the year, where the maximum rate of the earth’s declination is about 0.4 degrees per day as given by the following equation:

284  n   23.45sin 360 (3-10) University365

Ultimately, for daily values, variables  and  represent hour angles given by the 1 2 following equation:

 cos1 tan tan  (3-11) 1,2  1,2   

3.3.2 (b) Clarity Index

The clarity index is a dimensionless parameter representing the quotient of global solar irradiation on a horizontal surface to the extraterrestrial solar irradiation. It is given by:

G Kt  (3-12) G0

The clarity index outcome partitions days according to three types: clear, partially cloudy and cloudy. The range of clarity index values corresponding to such days is 0.6-

0.8 for clear days and 0.1-0.3 for cloudy days. However, weather is called partially cloudy if the value is between 0.2-0.6. Therefore, the clarity index can provide an impression regarding the type of collector device that can be used to collect solar energy. For example, the PV which use concentrate solar energy technology prefers locations with clear days, then clear days are presentMalaya in the clarity index. However, in the present work, the clarity index is used to find components of global radiation (direct and diffuse components). of

3.3.2 (c) Direct and Diffuse Solar Radiation

In solar radiation application, the global solar radiation must be resolved into beam and diffuse radiation. This is because most monitoring stations record global solar radiation.

In the present study, the correlation developed by (Erbs et al., 1982) is used to find the diffuse fraction component known as diffusion index, which is defined as a function of theUniversity clarity index and is shown by the following equation. 1 0.09Kt...... for, Kt 0.22

Gd  2 3 4 Kd 0.9511  0.1604Kt  4.388 Kt  16.638  Kt  12.336  Kt  ... for ,0.22 Kt  0.8 (3-13) G  0.165...... for, Kt  0.8

The beam radiation can then be estimated by subtracting the diffuse radiation (Gd) from the global solar radiation (G) as follows:

GGGbd (3-14)

Ultimately, the total solar radiation incident on solar PV or any application can be estimated based on the model developed by (Reindl et al., 1990). This model is also recommended by the American National Renewable Energy Lab and is defined as:

1 cos  3  1 cos   GGGARGAT b  d i  b  d 1  i  1 f sin  Gg   (3-15) 2 2 2 

where Rb represents the ratio between beam radiation on a tilted surface to the beam radiation on a horizontal surface. This factor is given by:

cos Rb  (3-16) cosz

Ai is called the anisotropy index, and it refers to the transmittance of beam radiation.

This factor is normally used to estimate the amount of circumsolar radiation, and in some references it is called forward scatter radiation.Malaya It is given as: G A  (3-17) i G 0 of Factor f is called horizon brightening, and it indicates that most of the diffuse solar radiation comes from the horizon. However, it is related to sky cloudiness and is given by the following equation:

Gb f  (3-18) G0

3.3.3 Photovoltaic Model Assumptions AnalysisUniversity of the PV generator requires input of solar radiation, weather temperature and wind speed data. A fixed PV generator system was modelled, which means that the system does not track the sun’s position or move its solar panels to face an optimized aspect position. The slope angle, defined relative to the horizontal of the PV mounted system (or latitude of the Earth), was fixed at zero degrees in order to maximize annual solar energy production. The azimuth angle, which defines the direction the solar panels

65 are facing, is commonly fixed towards the equator. However, in this case since the slope is zero, the azimuth angle is insignificant. Another factor considered is the solar derating factor, which has been obtained. The Ground reflectance (Albedo) is defined as the reflectance radiation from the surface divided by the incident radiation upon it, as shown in Equation (3-19). Since Peninsular Malaysia is a tropical rainforest region

(according to Koppen climate classification), ground reflectance is considered 0.2, which represents the typical value (Page, 2003; CDI.info, 2010). Last but not lest we will assume that solar PV will give same power through project lifespan.

Gref   (3-19) Gins

3.3.4 PV System Cost Analysis In general, there is a dramatic difference between renewableMalaya and non-renewable energy cost characteristics. While traditional, non-renewable energy sources tend to have low capital cost and high operating cost, conventionalof renewable energy sources tend to have high capital cost and low operating cost. Therefore, in financial analysis both capital and operating costs through the project life span must be considered.

To assess the feasibility of grid-connected PV, two key economic indicators are projected: the total net present cost and the levelised cost of energy. The Levelised Cost of Energy (LCOE) is simply the cost at which energy must be generated from a source to break even over the project lifetime (Mathew, 2006). This key economic indicator is definedUniversity by equation (3-20).

CAnn, total LCOE  (3-20) Esyst, out

The total net present cost (NPC) condenses all costs of system components and revenue within the project lifetime; therefore, it is used to rank the entire system configuration

66 according to the maximum and minimum cost. This key economic indicator is defined by equation (3-21).

C C  Ann, total (3-21) NPC CRF

The total annualized output power (Esyst, out) is equal to the sum of the power served, saved, and sold (feed-in tariff). The annual energy produced by the system can be estimated by multiplying the output power during the system’s lifespan. The total annualized cost (CAnn,total) of the system can be estimated by multiplying the net present cost of the WECS (over its lifetime) by the capital recovery factor:

CCRFAnn, Total C  NPC (3-22)

The net present cost of any system is equal to the sum of the total fixed and variable costs. The total fixed cost is equal to the amount invested,Malaya which is capital cost (CCap) and occurs at the start of the project (zero years). The variable cost consists of expenses resulting from operation and maintenanceof (O&M). So, this depends both on the RE system’s continuous operating costs through its lifespan and its reliability (e.g. wear and tear). It is given by the following equation:

CCCCNPC Cap  O& M  Salv  (3-23) Equation (3-23) shows that the total annualized cost is a function of the capital recovery factor (CRF), capital investment cost (CCap), O&M cost (CO&M), salvage value (CSalv), and discount rate (λ). The capital recovery factor (CRF) is the present value of the series of Universitycash flows through the system’s lifespan, and it is a function of real interest rate and project lifespan, as determined by equation (3-24) (Grant et al., 1990; Park and Tippett,

1999):

rr1 n CRF  (3-24) 11r n

The capital investment cost (CCap) is the summation of the PV cost (CPV,C), installation work (CInst,C), supporting structures cost (CSupp,C), and miscellaneous components cost

(CMisc,C) (e.g. electrical inverters, control panels etc.), as shown in equation (3-25).

CCap CCCC PV,, C Inst C Supp, C Misc, C (3-25)

The share of each component is presented in Figure 3.7, which shows a typical cost breakdown for a grid connected solar PV project (Kannan et al. 2006). This breakdown is used in the project cost-analysis done in this study.

Malaya Figure 3.7 Distribution of PV capital cost Discount rate (  ) refers to the discount inof cash flow throughout the project lifespan, easily because the current value of money is less than its future value. This is since interest can be earned if the money is put into an eligible depository institution.

Discount rate is given by the following equation (Adaramola et al., 2011; Paul et al.,

2012):

n 1 1 e  1 om&  (3-26) r eom& 1 r

EquationUniversity (3-26) shows that the discount rate ( ) is a function of the operating and maintenance escalation rate (eo&m), real interest rate (r), and project lifespan (n). The real interest rate is adjusted for the time value of money and risk (or uncertainty) of anticipating future cash flow. Therefore, it shows the actual change in purchasing power in investments. The real interest rate is given by equation (3-27).

if r  (3-27) 1 f

Equation (3-27) shows that the real interest rate is a function of the inflation rate (f) which shows the rise in prices of goods and services over a certain period of time. The nominal interest rate (i) is approximately the rate an investor receives from the bank if they deposit money (Adaramola et al., 2011).

However, by taking the impact of inflation escalation rate (e) of operation and maintenance through the project life span, the inflation rate ( f ) in equation (3-27) can be corrected and becomes known as apparent inflation rate. The inflation escalation rate

(e) using the Irving Fisher equation becomes the corrected ( f ) form as (Adaramola,

Paul et al. 2011): fef111  Malaya (3-28) The operation and maintenance cost (CO&Mof) is an essential issue in any project, because some components (inverter, connection joint, wires, etc.) are prone to wear and tear throughout the lifespan. In economics analysis, the O&M cost accounts for the first year of the project lifespan as a reference year. However, the changing value of O&M through the project lifespan appears in the discount rate, which is presented in Equation

(3-27). Last but not least, the salvage value (CSalv) represents the junked sale value of the remaining components after the system’s lifespan has been expanded (Ohunakin, et al.,University 2013). Last but not least, there is a need to know the payback period during economic analysis of any renewable-energy system. The bay back period is given by the following equation:

C ($) Payback  NPC (3-29) years $ Energy Produce ( kWh / year )  Incentives ( ) pV kWh

The incentives in equation (3-29) represent typically the unit cost of electricity bill, which is received each month. However, in case of any support program such as FiT then incentives in the above equation represent FiT rate ($) per kWh.

3.3.5 Cost Analysis Assumptions

The cost analysis presents by the following work developed using some assumptions.

For example, there are many types of PV panels with different efficiencies and prices, but as mentioned above, the present work focuses on crystalline silicon (C-Si) PV module. This is because C-Si represents 85-90% of the current global annual market

(International Energy Agency, 2010). Moderate prices for 2013 developed by the

National Renewable Energy Laboratory have been used and are shown in Table 3.4

(NREL, 2013). The project lifetime is assumed to be 21 years as the warranty of feed in tariff incentives is 21 year. However, the PV panels’Malaya lifetime is assumed to be 25 years as it comes with a 25-year performance warranty. While, the inverter lifetime is assumed to be 12.5 years according to surveysof showing that lifespan is normally 10–15 years for medium quality. However, the capital cost of the inverter is about 11% of the total cost of the entire system’s component costs (Figure 3.7). The annual O&M cost is assumed to correspond to the total installed cost (Table 3.4).

Table 3.4 Range of capital-specific costs of solar PV based on the rated power (NREL 2013).

Technology Type Installed Installed cost Fixed O&M Fixed O&M cost Std. Dev. Std. Dev. UniversityMean ($/kW) (+/- $/kW) ($/kW-yr) (+/- $/kW-yr) PV <10 kW $3,910 $921 $21 $20

PV 10–100 kW $3,819 $888 $19 $18

PV 100–1,000 kW $3,344 $697 $19 $15

PV 1–10 MW $2,667 $763 $20 $10

An annual interest rate of 6% is assumed. The appropriate value of this variable depends on the current macroeconomic condition, plus additional factors such as financial strength, implementation and other policy incentives, but for renewable energy projects, 6% is recommended (Givler and Lilienthal, 2005). The buyback period is calculated according to feed-in-tariff (FiT) incentives developed by Malaysia's government, Fit’s fixed rate for solar PV of 1.23-0.85 RM/kWh, with annual digression of 8% and additional FiT rates will be given for those RE installations that meet the criteria entitling it to additional bonus FiT rates between 0.26-0.01 RM/kWh as shown in Table 3.5 (Hashim and Ho, 2011). Lastly, in LCOE analysis it is assumed that each

PV system will produce the same power through the project lifespan.

Table 3.5 Solar Energy tariff in Malaysia proposed for 2011

In Annual Renewable Description of Effective Malaysia digression Resource qualifying ... Period Malaya(RM/kWh) rate Solar PV Less Than 4 kW 21 1.23 -8% Between 4 kW-24 kW of21 1.20 -8% Between 24 kW-72 KW 21 1.18 -8%

Between 72 kW-1 MW 21 1.14 -8%

Between 1 MW-10 MW 21 0.95 -8%

Between 10 MW-30 MW 21 0.85 -8%

Bonus for rooftop 21 0.26 -8%

Bonus for BIPV 21 0.25 -8% University Bonus for local modal 21 0.03 -8% Bonus for local inverters 21 0.01 -8%

3.4 Modeling Solar PV of Street Light Applications

The main purpose of this work is to investigate a PV scenario for electricity production, which will help the Malaysian government fulfil obligations and reduce greenhouse emissions. In this context, full understanding of any renewable-energy products will

71 accelerate this project. Therefore, the feasibility of PV for electricity production in street light application in remote areas is also investigated in an attempt to create another investment domain and highlight this application to policy makers.

Street lighting has been considered essential for road safety and crime prevention since the Middle Ages. The technology has progressed a long way from the simple oil lamps used by these olden civilizations. In modern societies, street lighting can be considered a measure of the quality of life (Farrington et al., 2002). In rural or remote areas, lighting is often difficult to handle due to grid extension costs and inaccessibility. These problems highlight the need for stand-alone lighting powered by sustainable, renewable and clean energy sources. Modern street lights generally have an automated switch that only provides the lamps with power when the level of daylight reduces beyond a prescribed intensity (e.g. night or during adverseMalaya weather conditions). Automation conserves energy and costs, because no power is expended when street lights are not functioning. In the present work, several Stand-Aloneof Pole Street Light (SPSL) models have been developed to meet this requirement. Models with renewable energy sources

(RES) are generally based on photovoltaic (PV) generators (commonly known as solar power), wind turbine generators, or an integrated combination of these two systems

(PV-Wind). Different configurations of these models have been developed with varying pole heights (normally between 8-15 m), illumination intensities (optimized for urban or country regions), and optimizations for operation in windy or sunny regions (Made-in-

China-Com,University 2012). Modeling SPSL is summarized by the following subsections.

3.4.1 SPSL Model Description

In designing a renewable energy system for SPSL, the required electrical load must first be determined because it has direct impact on its cost, size, and feasibility. The capacity of the energy storage component (e.g. battery) depends on how many hours the SPSL must operate each day, so it is imperative to determine the distribution of hourly load

72 throughout the year. In this analysis, the typical consumption demand of one SPSL

(with a 112 LED model LU4) was 121 W.

The power load of a fixed number (N) of SPSLs illuminating a street (positioned in two staggered rows) can be simply estimated from this by Equation (3-29).

Total Power 1 21  NW (3-30)

3.4.1(a) Battery and Controller

An SPSL is powered directly by a battery, which is autonomously recharged by a renewable energy system (RES). It is impractical to directly provide power to the SPSL by an RES without a battery. Since weather conditions vary with time, the power produced by an RES (e.g. PV panels or wind turbines) fluctuates correspondingly. Also a PV generator cannot provide power at night. TheMalaya battery works as an energy buffer device and is sized to meet the disparity between energy consumption and production by the RES. In this analysis, a lead-lead dioxideof 6FM200D-X battery (12 V, 200 Ah) made by Vision-Battery Company was used, which has a float life of 10 years (Group,

2011). The battery stack may contain a number of batteries per string, such as (0, 2, 4,

5, 6, 8, 24 or 32) to match the SPSL energy demand. HOMER models the battery as a two-tank energy storage device, where the available tank can be immediately charged or discharged; however, a bound tank can only be charged or discharged at a limited rate.

A two-tank system model that best fits the charge characteristics curve of the 6FM200D-XUniversity battery was used in the analysis.

Sunlight brightness and wind speed fluctuations produce high voltages that can damage batteries if left unchecked. A charge controller is used to limit the charging voltage. The

HOMER model allows switching between system components, thus controlling the power flow through the system to prevent devices working in parallel.

3.4.1(b) Photovoltaic (PV) Generator

For street light application, the PV panel model KD140GX-LPU is utilized

(Wholesolarsale, 2011). PV generator analysis using HOMER requires the input of measured solar radiation data. A fixed PV generator system was modelled, which means that the system does not track the sun’s position and move its solar panels to face the sun in an optimized direction. The slope angle, defined relative to the mounted horizontal (or latitude of the Earth), was fixed at zero degrees in order to maximize annual solar energy production. The azimuth angle, defining the direction the solar panels are facing, is commonly fixed with orientation towards the equator. But in this case, since the slope is zero the azimuth angle is insignificant. Ground reflectance was considered 0.2, which represents the typical value (Page, 2003). The replacement cost is assumed to be zero, since the solar model has an expectedMalaya rated lifetime equal to that of the entire system (20-25 years) (Wholesolarsale, 2011).

3.4.1(c) Wind Turbine Generator of

Wind turbines convert wind energy to electrical power. The capacities of any wind turbine depend on its swept area and hub elevation. Mini, small-scale wind turbines

(with rated power of less than 1 kW) can be used with SPSL. The power output curve of a wind turbine is defined by three velocities: cut-in, cut-off and rated velocity. The turbine is able to produce power when the wind speed rises above its rated cut-in speed.

As the wind speed increases, the power production also increases until a maximum averageUniversity power is achieved, called the rated power. The wind speed needed to produce this is called the rated velocity. As wind speed further increases, it will reach a cut-off velocity, and the wind turbine will shut down to prevent damage to the turbine. The

HOMER program was used to calculate the performance of a wind turbine by inputting its characteristic power output curve and the measured hourly input wind speed data obtained from a meteorological station. The SW-Air-X produced by the Southwest

Wind Power Company was analyzed in the present study, as this type of wind turbine is suitable for small charge applications (power, 2012). The lifetime expectancy of this turbine is assumed to be equal to the lifetime of the whole system (20 years), so the replacement cost is assumed to be zero since the wind turbine lifetime is equal to that of the project (power, 2012).

3.4.2 SPSL Model Assumptions

In this application, moderate prices of street light components for 2012 were considered

(Table 3.6). The selected field of study was Penang Island, which is located on the northwest coast of Peninsular Malaysia.It was chosen because it has high solar radiation potential, meaning that our analysis will be based on a better location for energy resources. So, if the analysis indicates positive results, there will be a need to extension the study, but if they are negative, there will be no needMalaya to investigate another location. Table 3.6 Specifications of SPSL components utilized in the proposed models

Parts Model ofLifetime Specifications Prices , USD

LED Street Light LU4 50,000 hr. 112W/24V 410

Solar Panel KD140GX-LPU 20 years 140W/12VDC 306

Wind Turbine SW-AIR-X(3) 20 years 550W/12VDC 895

Battery 6FM200D-X 10 years 200AH/12V 406

Controller 600W/24V 20 years 600W/24V 500

Total solar panel- 167

installationUniversity

Others 5%

3.5 Acceptability of Renewable Energy Policy

Many scholars worldwide agree that implementing renewable energy (RE) technologies and achieving a government energy policy is not feasible without social acceptance

(Mallett, 2007; Wüstenhagen et al., 2007). Hence, even if the government of Malaysia has declared the feed-in-tariff as a type of support to promote RE applications, especially grid-connected PV, there remains a need to investigate people’s opinions regarding solar PV application and overcome this problem. However, examining the success of RE programs was previously developed in the country in order to increase people’s awareness about solar energy application.

The methodology used to investigate social acceptance is the survey questionnaire (such as used by Munjur, 2013). It comprises 14 questions and extra questions on personal information, such as name, age, education and city.Malaya Internally, the survey includes questions at three levels. The first level is to test people’s knowledge of general issues, such as global warming; the second level isof meant to test what the people’s notions and knowledge are regarding solar energy potential in Malaysia. The third questionnaire level includes more technical questions developed to measure the success of informative promotional features. These are displayed in printed and electronic media by the government and associated commercial sectors to help people gain sufficient knowledge regarding the feed-in-tariff. In this context, the survey was distributed to the general public with different levels of knowledge and standards of living, such as workers,University business people, employers, shoppers, employees, and people living in different states. The data was collected from 580 respondents, and their response expectations and prospective carry a weight in highlighting obstacles and setting fresh key parameters for modifying current policies or developing new successful solar energy policies. The survey questions and results will be presented in sequence through a subsequent discussion. The survey questions are shown in Appendix D (Part A).

CHAPTER 4: RESULTS AND DISCUSSION

4.1 Introduction

This chapter discusses all the results starting with how the Artificial Neural Network was accurately used to predict the solar radiation data in more than 341 cities and 292 extra locations, which had a complex topography. However, how the predicted solar radiation database was used to rationally develop the solar radiation maps using the

ArcGis technique. All of which were priority wise ranked as being crucial to the associated beneficiaries, such as, designers, investors, the financiers, and home owners, etc., who are willing to invest in the solar energy plants. In addition, the performance and cost of the grid-connected applications are investigated in this chapter based on the economic indicators, such as, net present cost, level cost of energy and payback period. Nonetheless, the potential benefits from Feed-in-TariffMalaya new support mechanism in the grid connected PV applications are also investigated. The impacts of numerous local parameters on the performances and cost ofof the grid connected solar PV are individually highlighted as the most important factors which, if not appropriately monitored, can adversely upset the cost of energy. The feasibility of PV for electricity production in hybrid stand-alone street light application is also investigated for installation in the remote areas. The survey which is used to realistically gauge the Malaysian public’s awareness and perceived importance of the solar energy usage and the full understanding on the information with regards to the Feed-in-Tariff incentives and the mainUniversity obstacles are likewise investigated. The following subsections summarize and highlight the targets of the current study.

4.2 Assessment of Solar Radiation

Artificial neural network models were developed to describe the complex relationships between solar radiation and other meteorological parameters. However, these models can be fruitfully used to predict new solar radiation data at distant locations. The

77 prediction database is expected be used to develop effective and adequate solar radiation maps. The following subsections summarize and highlight the targets of the current study.

4.2.1 Solar Radiation Prediction

The training process for all developed networks was investigated and methodically checked for improvement in order to achieve optimum performance. Error training and testing was done using Mean Absolute Percentage Error (MAPE), Mean Square Error

(RMSE) and Correlation Coefficient (R). The analyses on statistical errors are presented in Table 4.1 for the 12 designated networks to show the difference between monthly mean daily solar radiation during the dataset training and testing periods. The optimized model was used to predict solar radiation in 341 cities.

Based on the training dataset for 12 models, it was foundMalaya that MAPE range from 2.15 % to 5.69%, and RMSE from 0.1363 to 0.3354,of with maximum correlation factor (R) of 0.97. The corresponding ranges for the testing dataset were from 3.20 % to 6.08% and from 0.1985 to 0.3712 respectively with the maximum R being 95.39. There were between 1 and 2 hidden layers and between 3 to 14 neurons. The number of iterations during training ranged between 6 and 41 epochs. For solar radiation predictions, the optimum networks among all 12 models were obtained with Model#3 are shown in

Table 4.1. The correlation coefficients (R) between the predicted and measured solar radiationsUniversity during training for the entire dataset of Model#3 (Table 4.1) are shown in Figure 4.1.

One of the stations used for testing is the Petaling Jaya station, as seen in Figure 4.2.

The differences between the maximum, minimum, and mean MAPE values for predicted solar radiation were 2.8836%, 3.919% and 3.4164%, respectively while the

78 standard deviation was 1.7355. These error ranges are quite acceptable in terms of weather data prediction.

Figure 4.1 Correlation coefficient (R) between predicted and measured solar radiation during the training process of Model#3, where read the line represents the measured data and blue line is the best fit for predicted data. Malaya Similar ANN networks have also been applied elsewhere to predict solar radiation and wind speed using many models with differentof input parameters and numbers of neurons. For purposes of performance comparisons with other countries, which have different topography, it seems that the Max and Min asset values of MAPE are 19.1% and 6.5% in Saudi Arabia (Mohandes et al., 1998), 12.5% and 0.03% in India (Reddy and Ranjan,

2003), 6.73% and 1.72% in Turkey (Sözen et al., 2004), and 25.3% and 8.9% in Nigeria

(Fadare, 2010). In our study, the maximum and minimum errors are 6.07% and 3.2% respectively, which show that it is possible to effectively control the maximum error to belowUniversity that of other countries. The network models (Table 4.1) are optimized by varying the number of neurons i.e., 1,

2……49, 50 neurons) in each training round and then selecting the most accurate result.

Each time a value is obtained the associated errors (Equations 3-2, 3-3 and 3-4) are computed.

Figure 4.2 Comparison between solar radiation predicted and measured for Petaling Jaya station. Table 4.1 Different network performance based onMalaya the training and testing datasets.

No. of No. of Number Network Training Neurons Training Dataset Test Dataset Hidden of in of Model Hidden MAPE R- MAPE R- Algorithm Layers Iterations RMSE RMSE No. layer (%) value (%) value 1 LM 1 3 6 0.2249 3.00 0.919 0.2200 3.93 0.939

2 LM 1 7 29 0.1801 2.47 0.949 0.2903 5.28 0.921

3 LM 1 14 6 0.1501 4.05 0.965 0.1985 3.20 0.954

4 LM 2 3 6 0.2378 3.40 0.910 0.2561 4.45 0.921

5 LM 2 7 11 0.1979 2.16 0.939 0.2427 4.39 0.930

6 LM 2 14 41 0.1469 4.70 0.966 0.3712 6.08 0.894

7 SCG 1 3 16 0.2752 2.47 0.876 0.2769 4.94 0.899 8University SCG 1 7 33 0.1488 2.26 0.965 0.3083 5.79 0.916 9 SCG 1 14 17 0.1363 5.69 0.971 0.2564 4.52 0.930

10 SCG 2 3 18 0.3354 3.99 0.822 0.3499 6.07 0.840

11 SCG 2 7 25 0.2362 4.80 0.910 0.2399 4.41 0.929

12 SCG 2 14 23 0.2362 4.80 0.874 0.2710 4.72 0.913

SCG means scaled conjugate gradient algorithms and (LM) means Levenberg-Marquardt (LM) learning algorithms.

4.2.2 Solar Radiation Mapping

After the development of artificial neural networks, these were systematically trained with sufficient precision. Then, the best model was used to predict solar radiation in 341 cities in Peninsular Malaysia. The geographical descriptions of selected cities obtained from the Meteorological Department of Malaysia are presented in Appendix A (Tables

A1-A12), which also coincide with yearly predicted solar radiation and clearness index.

However, to increase the accuracy of the solar radiation maps, ANN was used to extend the predicted monthly mean solar radiation to 292 extra locations. These extra points were obtained from the Malaysian topographic raster map.

The geographical information system software (ArcGIS) version 9.2 was used to develop solar maps from January to December. The main purposes of the solar maps are to visualize vividly the locations which are sufficientMalaya to be used for the installation of solar applications. The annual monthly mean daily solar radiations are presented in Figure 4.3 so as to coincide with the landof mass topography and enable a visual comparison. In addition, some of the selected solar maps are presented in Figure 4.4, while all solar maps are presented in Appendix B (Figures B1-B12). Finally, monthly average solar radiations (mean, maximum and minimum) for all months are mapped out as presented in Table 4.2. It has been found through statistical analyses of the solar maps that the monthly averages of the daily solar radiation potentials vary about 26% throughout the year on a monthly basis. The predicted solar radiation maps shows that PeninsularUniversity Malaysia receives a monthly average of daily solar radiation ranging from 3.82 to 5.23 kWh/m2/day throughout the year. The highest solar radiation is estimated to be between 5.79 and 5.92 kWh/m2/day in February, March and April, while the lowest is estimated to be between 3.33 and 3.40 kWh/m2/day in November and December, respectively.

Table 4.2 Statistics of predicted monthly average solar radiation in Peninsular Malaysia

Monthly mean daily solar radiation (kWh/m2/day) Month Minimum Maximum Mean Std. January 3.82 5.33 4.58 0.36 February 4.14 5.79 5.12 0.28 March 3.97 5.87 5.23 0.34 April 4.03 5.92 5.18 0.30 May 4.01 5.73 4.97 0.26 June 3.93 5.67 4.78 0.32 July 3.81 5.22 4.69 0.21 August 3.78 5.13 4.56 0.21 September 3.61 5.20 4.66 0.22 October 3.39 4.90 4.49 0.21 November 3.40 4.76 4.13 0.25 December 3.33 4.75 3.82 0.31 Yearly 3.77 5.36 Malaya4.68 0.27

However, as shown in Figure 4.3, for the ofannual average monthly mean of daily solar radiation in the extreme northern coastal region, especially in the West Coast line of

Peninsular Malaysia, there is the highest potential for solar energy application due to high solar radiation throughout the months. Nonetheless, it is worth noting that solar radiation maps vividly show the adverse impacts of the area’s physical characteristics on the solar radiation maps, such as the mountain range which induces the frequent formation of cloud masses over the area. This issue is obvious if we choose to compare theUniversity solar energy stations at Cameron Highlands, located on a high land at 1545m elevation. The nearest is the Ipoh station, which is located on a relatively low lying land at an elevation of 40m and where a surprising gradient in the amount of solar radiation is evident. A logical explanation is the weather variability structure in those regions that is naturally controlled by the areas’ physical attributes Figure 4.3.

(a) Malaya of

University

(b) Figure 4.3 Comparison between: (a) the annual global solar radiation incident on the ground , (b) the complex terrain of Peninsular Malaysia

(a) Malaya(b)

University

(c) (d) Figure 4.4 Solar radiation maps for the monthly mean of daily solar insolation (kWh/m2/day) incident on a horizontal surface in peninsular Malaysia for (a) January, (b) April, (c) July, and (d) October

4.3 Feasibility of PV electricity in Peninsular Malaysia

Three steps used to assess the feasibility of solar PV in Peninsular Malaysia. Firstly; conduct a critical analysis on the solar radiation potential in the selected states.

Secondly; estimate the energy that can be generated using the grid-connected solar PV system in those locations. Thirdly; evaluate the economic indicators, such as: the level cost of energy and payback periods, with taking into account the Feed-in-Tariff as a current available support mechanism. The results of analyses are summarized in the following four subsections.

4.3.1 Solar Radiation Intensity in Peninsular Malaysia

The analysis of seasonal variation of monthly mean daily global solar radiation and clearness index for 24 meteorological stations are shown in Figure 4.5. Some interesting characteristics are noted, which is the one season. However,Malaya the analysis shows no large difference between high and low intensity of monthly solar radiations of the tropics weather. The highest value of monthly averageof daily solar radiation is 5.271 kWh/m2 occurs in March. While, the lowest is 3.691 kWh/m2 occurs in December.

University

Figure 4.5 Seasonal variations of solar radiation and clearness index in Malaysia.

The maximum clearness index over peninsular Malaysia is 0.49, while the minimum is

0.38. In the same context, between all, Lubok Merbau City followed by Alor Setar may be the best candidature locations for solar radiation application as shown in Figure 4.6.

Meanwhile, Cameron Highlands, which represents a mountain range location, is the worst candidature for solar radiation application.

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Figure 4.6 Seasonal variations of solar radiation and clearness index

4.3.2 PV Electricity Generated in Peninsular Malaysia

The energy output of the PV system follows a site’s conditions, such as: the given weather temperature, solar radiation and corresponding clearness index. Some of the energy produced is lost in the inverter during the DC-AC conversion. The expression CapacityUniversity Factor (CF) is normally used to define the power output from the PV system.

It is simply represents the power produced by the power station to its rated power (Wp).

The locations according to CF are typically categorized into three groups: first, locations with extreme irradiation (more 2000 kWh/kWp), i.e. a 23% capacity factor; second, locations with low irradiation (less 1000 kWh/kWp), equivalent to 11%

86 capacity factor; and third, locations with moderate solar irradiation (between 2000-1000 kWh/kWp) (International Energy Agency, 2010).

Analysis of solar PV renewable energy produced in Peninsular Malaysia using equation

(3-15) shows a minimum value of 1010.13 kWh/kWp occurs in Cameron Highlands.

While, the maximum value of 1312.07 kWh/kWp occurs in Lubok Merbau city. In the same context, the corresponding capacity factors in both locations are 11.5% and 15% respectively (Figure 4.7). Hence, the solar radiation in Peninsular Malaysia is categorized between low and medium according to classification of PV electric production shown above.

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Figure 4.7 Average energy production each year and capacity factor.

4.3.3 Economic Indicators Utilizing PV System

Analysis level cost of energy using Equation (3-20), show that Cameron Highlands has theUniversity highest energy production cost of about $0.36 /kWh. Meanwhile, cities in the north of Peninsular Malaysia, such as: Chuping, Alor Setar, Lubok Merbau and Penang have the lowest production cost of about $0.28/kWh (Figure 4.8). The average cost of grid- connected solar PV energy in Peninsular Malaysia is $0.31/kWh, and the difference between high and low cost of LCOE is 25%. Maximum capital and net present costs of grid-connected solar PV, have capacity less than 10kWp, are $4831/kWp and

$5327/kWp respectively. Meanwhile, minimum capital and net present costs of the system are $2989/kWp and $3340/kWp respectively.

However, by comparing unit energy cost of both electric-powered grid and solar PV powering a grid in Malaysia, clearly the average cost of PV energy is $0.31/kWh (31

US cents/kWh), while the average cost of the electric grid on average is $ 0.12/kWh (12

US cents/kWh) (KeTTHA 2013). The comparison refers that the current cost of energy supplied by PV is about two times half more than the grid utility cost.

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Figure 4.8 Comparison of energy cost levels and capacity factor at selected sites.

Regarding to the payback period of the PV grid-connected system (equation 3-29) , and according to the unit cost of energy, which is the unit cost of the electricity bill in

Peninsular Malaysia (on average 12 US cents/kWh), the normal payback period of consumerUniversity utilizes grid-connected solar PV system will be between 23 and 37 years. Meanwhile, incentive of the government under Feed-in-Tariff was attractive in reducing the payback period. This is clear through analysis of both energy and cost of grid- connected solar PV system. It has been noted that the new average payback period in

Peninsular Malaysia ranges from a minimum period of 7.4 years in Beyan Lepas city to a maximum period of 9.6 years in the Cameron Highlands (Figure 4.9). While, the average payback period in Peninsular Malaysia due to the government incentives is

88 recoverable within a period of 8 years. Hence, payback period is reduced by government by approximately 73% or more result in a much more favorable economy for the investors.

Based on the minimum cost analysis of net present of the solar PV system in Peninsular

Malaysia, the lowest payback period will most likely be 6.5 years. Question here is?

Will the investor or home owner be able to use a cheaper technology to obtain a shorter minimum payback period? But the answer still remains that, is the cheapest PV system is rigid enough to stand the wear and tear through the project life span of more than 20 years and constantly capable of delivering the same energy supply? Hence, it is highly recommended that the associated companies should guarantee the continuous production of solar PV energy during the PV's lifespan, especially, if the components are fully used in the Feed-in-Tariff program. Malaya of

UniversityFigure 4.9 Comparison of payback period of selected sites

Finally, solar resource, energy and cost analysis of grid-connected solar PV in

Peninsular Malaysia show that the solar radiation varied between a minimum of 3.91 kW/m2/day and a maximum of 5.23 kW/m2/day, and the energy produced varied between 1.010 MWh/kWp/year and 1.312MWh/kWp/year. The CF varied between 11.5 and 15.5%, and the cost of electricity ranged between $0.28/kW) and $0.34/kW.

For purpose of comparison with other countries, we developed this analysis per kWp system capacity and currency in US dollar. In Greece, the solar radiation varied between a minimum of 4.04 kW/m2/day and a maximum of 4.89 kW/m2/day. The renewable energy produced every year based on solar PV (mono-crystalline silicon type with 14.5% efficiency) varied between 1.667 MWh/kWp and 2.081 MWh/kWp. The capacity factor varied between 19.4 and 24.2%, and the cost of electricity ranged between $ 0.167 /kW and $ 0.208 /kW (Fantidis et al., 2013). The comparison between

Peninsular Malaysia and Greece shows an important fact, which is although the solar radiations in Peninsular Malaysia are more than the Greece but the energy produced from PV system is less. This is because the most solar radiations in Malaysia come from the diffused component which has less effect in energy production from the solar PV, vice versa in Greece where most energy comes fromMalaya beam component. This fact makes the cost of grid-connected solar PV system higher in Malaysia. For comparison with Bangladesh, it appearedof that the annual global solar radiation over Bangladesh varied between a minimum value of 4.51 kW/m2/day and maximum of 4.99 kW/m2/day based on the Geospatial toolkit, NASA solar radiation database. The renewable energy produced each year via solar PV mono-crystalline silicon type had

13.9% efficiency, and varied between 1.653 MWh/ kWp/ year and 1.844 MWh/ kWp

/year. The capacity factor varied between 18.8% and 21%, and the cost of electricity fluctuated between $ 0.17/kWp and $0.23 /kWp from the most appropriate location to theUniversity least attractive location respectively (Alam and Sadrul, 2011). This comparison also reflects that the unit cost of energy in Bangladesh is less than Malaysia. It is good to mention here that the uncertainty regarding to the prices of grid-connected PV system components in the market could have impact on the above comparison in the future.

4.3.4 Potential CO2 Abatement in Peninsular Malaysia

Solar PVs can provide further benefits to Malaysia in the form of CO2 reduction, as the energy generated by the PVs can replace the energy generated by the conventional methods, such as, using oil and coal. The average emission factor of CO2 for electricity production in Peninsular Malaysia is 0.7535 tCO2/MWh (Green-Tec-Malaysia, 2011).

Therefore, the results are obviously favorable by using the grid solar PV system with a capacity of 1 kWp each in Peninsular Malaysia. It will result in an average of GHG emission abatement of 0.90871 ton of CO2 per year and a foreseeable total of 22.72 tCO2 through the project lifespan of 25 years for each solar PV system.

4.4 Sensitivity Analysis of PV Parameters

The sensitivity analysis is required due to the different market options of renewable energy and uncertainties in the key parameters. TheseMalaya factors and options can be quite challenging as it has impacts on energy and level cost of energy (LCOE). One example, the energy produced by the solar PV is a offunction of the geographical site parameters which has an overwhelming impact on the estimated energy and published LCOE. In this context, and to make a critical sensitivity analysis for solar PV parameters, there was also a need to a specific location. In the present work, Kuala Lumpur (KL), the capital of Malaysia, selected to study the performance of Grid-connected PV. The sensitivity analytical results of the most important parameters are as presented in the subtitlesUniversity below starting with analysis solar resource. 4.4.1 Solar Resources in Kuala Lumpur

Kuala Lumpur (KL) is the federal capital, and the most populous city in Peninsular

Malaysia, which approximately covers an area of 243 km2 and an estimated population of 1.6 million as of 2012. It is in a tropical rainforest region, and the weather is generally sunny and hot and temperature ranging from 31°C to 33°C during the day, and from 22 to 24°C at night with plentiful rainfall. The weather data is recorded by

Subang Meteorological Station located at: 3°07’ north and 101°33' and 16.5m above sea level. Continuity of weather data without any lack is important and critical in analyzing solar PV performance. This is because the system will work continuously without power interruptions or cut-offs during sun shine duration. The analyses of the weather parameters show that the weather generally is cloudy or partially cloudy throughout the day with annual clearness index about 0.477. The rain is especially plentiful throughout the monsoon period lasting from April to September. The monthly average daily solar extra-terrestrial radiation over Kuala Lumpur was 9.92 kWh/m2/day, which is substantially high, but most of this radiation was scattered, absorbed and reflected due to the atmosphere and cloudy weather. Thus, the net incident residual solar radiation on the ground was only 4.74 kWh/m2/day. The solar radiation absorption, due to the atmosphere, through three typical days is as shown inMalaya Figure 4.10. of

Figure 4.10 Solar radiation absorption due to the atmosphere effect in Kuala Lumpur UniversityMalaysia. 4.4.2 Performance of Grid Connected Solar PV

Given the solar radiation, temperature and the wind speed, the calculation can start from estimating the integral incident solar radiations on the solar PV using equation (3-15) and then estimating the total energy production using equation (3-5). Through analysis of 8,760 hourly power output generated from domestic fixed tilt PV system; it was found that the energy output from the system was 1,165 kWh/kWp/year. The capacity

92 factor of the system was 13.3%. The maximum monthly energy produced was 106.9 kWh/kWp in May, and the minimum in November was 79.8 kWh/kWp. The maximum power output from the installed system was 0.68 kW/kWp and the system would work approximately for 4,328 hours in a year. The typical outputs of the developed solar PV system are shown in Table 4.3. During days, the highest temperature of PV cell plate was 76.63oC, meanwhile the maximum weather temperature was 34.4oC.

Table 4.3 Typical output of the developed solar PV system

Quantity Value Unit

Rated capacity 1.00 kW

Inverter capacity 1.00 kW

Mean output 0.13 kW Mean output 3.19 MalayakWh/day Capacity factor of13.3 % Total production 1165 kWh/year

Minimum output 0.00 kW

Maximum output 0.68 kW

Hours of operation 4,328 hour/year

Note: 1 US dollar ($) = 3.13 Ringgit (RM) during the period of study

4.4.3 Impact of Wind Speed on PV Energy

TheUniversity wind speed does not directly affect the solar cell output, but its effect on the temperature of the surface of the PV as shown in equations (3-7) and equations (3-8). It is work on decreasing the temperature of the solar cell through heat convection. Two models were developed to study the impact of the real wind speed on solar PV power production. In the first model, we assumed a slandered wind speed (1m/s), while in the second model, the real value of the wind speed is used. Through the analysis, it was

93 found that the maximum increase in power due to the impact of wind velocity was 3% through January (Average wind speed was 2.04 m/s), while the minimum was 1.67% in

November (Average wind speed was 1.2 m/s). The average monthly increase in power was 2.16%. This is relatively agreed with results elsewhere, which indicated that the increase of wind speed from 1m/s to 4.5 m/s could increase the solar PV efficiency by 4 to 5% (Brian Vick 2013). Hence, from current analysis, we can conclude that neglecting wind speed through power analysis will not result in a significant error.

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Figure 4.11 Weather temperature and PV cell temperature, with and without wind speed for the development of models through three continuous days.

For comparative purposes, Figure 4.11 shows the weather temperature and PV cell temperature with including the wind speed (Model-2) and excluding the wind speed

(Model-1). It clear from Figure 4.11 that the wind speed slightly reduces the PV cell temperature (green and red lines), and as the wind speed becomes less than the designed speedUniversity of 1m/s, this reflected an increasing PV cell temperature module as shown green line. However, analysis the difference between the weather temperature and solar cell temperature in Figure 4.11 reflects a fact that the solar/ thermal ( PV/T) can be used for heating systems, such as, water heating, especially, in the study where the house water supply was relatively cold. However, this could be also a potential benefit to enhance using solar PV.

4.4.4 Impact of Increasing System Capacity

Cost analysis shows that the capital cost of the system decrease with an increase in system capacity. For example, after estimated average NPC from equation (3-21) and

LCOE from Equation (3-20) for the system having a capacity of less than 10kWp in

Kuala Lumpur are $4,333/kWp and $0.31/kWh respectively. The sensitivity analysis of increasing system size shows the capacities between 1MW, and 10MW will have a competitive LCOE, where the average LCOE reduced to be $0.20 /kW as compared with $0.31/kWh for a system with a capacity of 10 kW. Under this circumstance, minimum NPC and LCOE are $3,340 /kWp and $0.24/kWh respectively. While, maximum are $5,327/kWp and $0.38/kWh respectively for the same system capacity

(Figure 4-12 (A)). Malaya of

Figure 4.12 (A) Expected LCOE for different sizes of solar PV system.

However, under feed-in-tariff incentives, the average payback period reduced as well dueUniversity to the increasing system size. The sensitivity analysis of increasing system size shows that a capacity between 1MW and 10MW has a competitive period, where the average period is 6 years as compared with 9.5 years for 10 kW systems as shown in

Figure 4-12 (B). Hence, increasing system size will reduce the payback period by approximately 37% resulting in much more favorable economies to the investors. This is because the initial capital cost in the current market of solar PV (Table 3.4) reduces with increasing system size per kWp (Table 3.4).

Figure 4.12 (B) Expected Payback period for different sizes of solar PV system.

In addition, the sensitivity of increasing the governmental incentives on reducing the payback period is also investigated. Figure 4.13 shows that the current payback period is at best condition when the incentive is at RM1.74/kWh ($0.55/kWh). It is means bonus roof-mounted PV systems under BIPV could make the average payback period approximately 6.5 years. However, increase in incentiveMalaya of up to RM2.4/kWh can reduce the average payback period of PVof system to 5 years in the case of Kuala Lumpur. The above results highly important if the government wants to decrease the current payback period in any future policy.

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Figure 4.13 Impact of FiT incentives on payback period for typical Solar PV system of (less than 10 kWp).

4.4.5 Impact of Ground reflectance

The reflectance from nearby surfaces would have an impact on the overall quantity of the solar radiation incident on solar PV system. The ground reflectance, or sometimes called albedo, measures the reflected fraction of the solar radiation incident on the ground. However, the Different surface has a different reflectance, for example; the typical value of albedo for grass-covered areas is around 20%, while a reflectance can be as high as 70% for snow-covered area, and these are the sample values used in calculating the radiation incident on the tilted PV panels. In the current work, the ground reflectance is assumed to be 0.25 according to the physical characteristics of most lands as mention before. The sensitivity analysis shows increasing the albedo from

0.2 to 0.7 can increase the solar PV energy by a small amount, only 0.25%. Hence, it has a modest effect on the LCOE. Malaya 4.4.6 Impact of Temperature Coefficient

The temperature coefficient of solar PV arrayof determines the strength of the PV power output due to the impact of surface temperature. However, it is a negative number because the power output decreases with increasing cell temperature. Manufacturers of

PV modules usually provide this coefficient in their product brochures, often labelled either as "temperature coefficient of power", "power temperature coefficient", or

"maximum power temperature coefficient". The typical value of this factor can greatly differ in different types of PV. For example, in thin film amorphous silicon and thin filmUniversity the difference can be as much as from -0.2%/C° to -0.6%/C° respectively. In the mono-crystalline and poly-crystalline PVs, the temperature coefficient was found to be small, that is, between -0.46%/C° and -0.48%/C° respectively. The sensitivity analysis of the temperature coefficient indicates that the changing the value of temperature coefficient from -0.6%/C° to -0.2%/C° can decrease the solar PV energy by 7.7 % for

97 amorphous silicon and thin film within this range . However, for mono-crystalline and poly-crystalline PVs, there is no considerable difference between both models.

4.4.7 Impact of PV slope and azimuth angles

The solar radiation incident on the solar PV can have a compound effect from the slope and azimuth angles of the PV panel due to the orientation of the mounted system relative to the horizontal. Normally, the slope 0° of the PV panel indicates the horizontal, while 90° indicates the vertical. In this context, if the slope approximately is equal to the latitude, this could maximize the yearly production of the PV energy in the fixed-slope systems. The azimuth angle defines in which direction the PV panels is, the direction towards south of 0° azimuth angle, due east of -90°, due west of 90°, and due north of 180°. However, the equator is indicated by 0° azimuth in the northern hemisphere, and 180° azimuth in the southern hemisphere,Malaya and is the point towards which the panels are almost always oriented in the fixed-azimuth systems. In this context, if the panels are mounted horizontallyof (zero slopes), the azimuth will then become insignificant. In the roof mounted system, the PV slope normally takes the pitch angle of the roof. The orientation of the roof can affect the sun angle on the module surface if the pitch of the roof is used. The sensitivity analysis of both azimuth and slope angles are summarized in Table 4.4, which shows that an array on a flat roof facing anywhere gives the greatest power output and smallest LCOE (correction factor of 1.00). This is because that the Peninsular Malaysia in the tropic. Herein, any increasingUniversity in the pitch angle can readily lead to an increase in cost of energy due to the reducing the energy of solar PV. For example, increasing the pitch angle up to 21/12 for

East-facing roof-mounted system will increase the LCOE by 1.4 (an increase of 40%) than that of a flat surface (0 pitch angle) because of reduction in the solar radiation incident on the PV. A maximum increase in the LCOE was recorded in the use of a vertical surface which was between 96% and 100% for an East-facing roof and a North-

98 facing roof respectively. Therefore, utilizing solar PV system mounting in a vertical wall will be costly.

Table 4.4 Percentage of change in the LCOE due to the orientation and roof pitches (or PV inclination & orientation for fixed system); a value of 1 means the current estimated value of the LCOE, whereas the author's value means the expected increase in the LCOE.

Flat 4:12 7:12 12:12 21:12 Vertical

South 1.00 1.03 1.08 1.20 1.40 1.96

SSE,SSW 1.00 1.03 1.09 1.21 1.39 1.97

SE,SW 1.00 1.04 1.09 1.20 1.37 1.85

ESE,WSW 1.00 1.04 1.09 1.20 1.34 1.75

E,W 1.00 1.04 1.09 1.20 1.33 1.71

ENE,WNW 1.00 1.04 1.10 1.20 1.34 1.72 NE,NW 1.00 1.04 1.10 Malaya1.21 1.38 1.82 NNE,NNW 1.00 1.04 1.10 1.22 1.41 1.95

North 1.00 1.03 1.09of 1.22 1.43 2.00

4.4.8 Impact of Real interest rate

The annual real interest rate shows the actual change in the purchasing power of the investment because it is adjusted to the time value of money and the risk or uncertainty of anticipation of future cash flow. In the present work, an annual interest rate of 6% is considered as it recommended feasibility studies of a renewable-energy system as mentionUniversity before. It is found through cash flow analysis that decreasing interest rate from

6% to 4% can decrease the LCOE by as much as 15.6%. Hence, annual interest rate drastically has a considerable impact on renewable-energy assessment.

4.4.9 Impact of Hypothetical Electric Rate Increase

The present section investigates the expected savings from using a typical domestic solar PV system of less than 4 kWp due to the hypothetical increasing unit cost of the

99 electricity bill. This is because the financial analysis is normally based on the current conditions of the market, and the unit cost of monthly electricity bills received by the home owners or consumers. Nevertheless, for the future, there is no guarantee of a non- increase in electricity unit cost.

Unit cost of the electricity bill in Peninsular Malaysia manages by TNB, and it is proportional to the electricity consumption due to the entirely domestic use. For example, for the first 200 kWh (1 to 200 kWh) per month, the cost is 6.8 US cents/kWh and for the next 100 kWh (201 to 300 kWh) per month, the cost is 10.4 US cents/kWh, but on the average, it is 12 US cents/kWh (39.3 RM cent/kWh) for monthly consumption of less than 1kWh per month (KeTTHA 2013). The sensitivity analysis shown in Table 4.5 estimates the expected savings from using a typical domestic solar PV system of less than 4 kWp in order to show Malaya the types of savings that may be achieved. However, gives a brief narration on the benefits to home owners and the estimate on reduction in electricity bills ifof the government decides to increase the unit cost of energy in the near future.

Table 4.5 Expected yearly benefit due to the hypothetical electricity rate increase

Solar Estimated Annual Utility Electric Energy Rate (US cent/kWh) Array Energy STC 10 15 20 25 30 35 1.00 kWP 1164.832 kWh 116$ 175$ 233$ 291$ 349$ 408$

2.00 kWP 2329.663 kWh 233$ 349$ 466$ 582$ 699$ 815$

3.00University kWP 3494.495 kWh 349$ 524$ 699$ 874$ 1048$ 1223$

4.00 kWP 4659.327 kWh 466$ 699$ 932$ 1165$ 1398$ 1631$

4.4.10 Experimental work and Validation Results in KL

The derating factor gives an indication about the difference between the work of solar

PV in the standard condition and out the lab in the real condition. Therefore, an experimental study is developed in UM Power Energy Dedicated Advance Centre

100

(UMPEDAC) building for three reasons; firstly, to investigate the real value of derating factor the typical days (cloudy, partially cloudy, rainy and clear day); Secondly, to study closely the stability of the grid-connected solar PV during the energy conversion;

Thirdly, to measure the maximum temperature could be reach by solar PV array while it was working.

The utilize system comprises from all PV array components in the actual solar PV grid connected system, such as: solar PV arrays, inverter; meter, switches. However, the measurement instruments and data acquisitions are connected together with the system used. In this context, a photovoltaic array with a capacity of 1.25 kWp developed by

Mitsubishi Electric Company ’type PV-AE125MF5N’ was connected in series to supply a 215 open circuit voltage. The PV system was connected to a DC-AC inverter ‘model SPI2000-B from EPV Company’ after which the outputMalaya of the inverter was fed to the global outside, the electric-powered grid, using an electric meter in between to record the energy produced as shown in Figure 4.14.of

University

Figure 4.14 Grid connected PV system and its components (the picture inside the energy analysis room). Meanwhile, to manage the AC and DC power, some recommended switch was put after the PV energy supply and before the grid energy supply. The energy estimation began by recording and analyzing the weather parameters, such as, incident energy on the

101 solar PV, weather temperature and wind speed. This information was recorded using a micro weather station model PRO2 from Davis Company (See Appendix C). However, to record the solar cell temperature during the day, two more sensors were put under the

PV plate; then, both sensors are connected to underneath PV array data logger type

HOBO from Onset Technology. The power produced from the PV was recorded using current and voltage sensors from Fluke–Technology; then, both sensors are connected to data logger type Picolog from Pico-Technology. However, before the inverter was connected to the data logger which was also connected directly to the PC computer. The total energy fed into the grid was estimated from daily digital meter readings. The

Schematic diagram of experimental setup is as shown in Figure 4.15. Malaya of

Figure 4.15 Schematic diagram of Grid-connected PV system AfterUniversity analyzing different typical days, it was found that the derating factor ranged from 88.7% to 72.5%. This is depending on the statues of weather (Rainy, cloudy, partially cloudy or clear), where the rainy weather has an impact on the decreasing the value of the derating factor. Therefore, the typical derating factor could be used for grid- connected solar PV application in Malaysia is 0.8. Through the analysis of the power supply, the system was drastically stable during the energy conversion, and no

102 overheated was noted in the inverter or in other system components. However, the relation between the solar radiation and the cell temperature are as presented in Figure

4.16, which shows a proportional relation between them. During the daily analysis, the maximum recorded temperature of the solar cell was 62oC, while the weather temperature ranged from 26oC to 28oC. However, the maximum solar radiation was

3.36 kWh/m2/day, while the minimum was 1.16 kWh/m2/day (rainy day).

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Figure 4.16 Solar radiations and solar cell temperatures through different Before considering any results, validate the energy produced by the developed system was recommended. Different techniques for validation results are considered, such as, using other modelling tools or using the curves and results of other literature reviews or sometimes by using the real operation data through installing PV grid-connected systems and taking the readings on typical days. In this context, the present work used theUniversity HOMER software to validate the results of the energy produced by developed solar PV model. As it is mentioned earlier that we developed two models, one of them without considering wind speed. The comparison in Figure 4.17 represents between the model without considering wind speed and HOMER. The analysis was based on the monthly average daily energy produced by the system in each hour. The comparison between the developed model (with considering wind speed” and HOMER model show

103 a slight difference in terms of energy production. The maximum difference is 2% while the minimum difference is 1% (Figure 4.17).

Figure 4.17 Comparison between the HOMER models and the developed model Malaya 4.5 Assessment of Street Light Applications To assess the feasibility of stand-alone poleof street light (SPSL), two models that have different power load schemes are considered. The first scheme requires a continuous operation at full power for 12 hours each day. The second scheme optimizes for energy savings, so that the system works at full power through the first 7 hours and at 66% (of full power) in the last 5 hours (totalling 12 hours). Each SPSL model was analysed using two different RES configurations: a photovoltaic (PV) generator or an integrated

PV/Wind-turbine generator system.

HOMERUniversity uses simulation, optimization, and sensitivity analysis to find the lowest lifecycle cost generation mix and explore how the optimum mix changes with different assumptions. During optimization, the models were sorted by the HOMER program according to the best least total net present cost of each model (NPC), so as to have a comprehensive view of the long-term system performance. The proposed models and the results of the study are shown in Figure 4.18 and Figure 4.19 respectively. The top

104 and bottom rows in these figures represent configurations sorted according to the best total NPC. These results show that the best SPSL configuration model is the PV model, for both the power load schemes.

Figure 4.18 Optimization results of the full load model which build in table categorizing and configuration models according to the best NPC.

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Figure 4.19 Optimization results of energy saving load Model which built in table categorizing and configuration models according to the best NPC.

The energy saving SPSL model comprises a 500W PV panel and 4 batteries (12V,

200Ah) per string). The total net present cost in the proposed system (NPC) was estimated at being $5,531. The energy savings mode allows a power reduction of 100 W,University giving a 10.4% reduction in NPC compared with the full load model. The total net present cost of the PV/Wind-turbine RES configuration was greater than the PV configuration due to the limited wind resources and the relatively high cost of the wind speed turbine. The cost of energy (COE), which is the average cost per kilowatt hour

(kWh) of electricity, in the energy saving model was $1.057 per kWh with an initial capital cost of $3878. The energy cost and initial capital cost was $1.018 per kWh and

105

$4,107, respectively. The renewable energy fraction is constant in all the optimization results, because the power supply comes entirely from renewable energy.

A study by the World Bank on rural electrification programs placed the average cost of grid extension (per km) between $5,000-$10,000, rising to $19,000-22,000 in difficult terrains (Marion, Adelstein et al. 2005; Group 2011).In this analysis, $8,000 was used as the reference value for the optimization process.

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Figure 4.20 Relation between NPC of full load model and grid extension cost.

The cost analysis in Figure 4.20 and Figure 4.21 shows that the break-even values occur at distances of 0.566km and 0.509 km for the full load and energy-saving load conditions. This is based on grid extension prices and total NPC of the PV model. These figuresUniversity also show that by fixing the grid extension cost, a SPSL powered by sustainable energy resources becomes more viable as the total NPC decreases. The analysis shows that illuminating streets by SPSL is feasible in remote areas, due to the otherwise high costs required to extend the grid to reach these locations.

106

Figure 4.21 Relation between the NPC of energy saving load model and the grid extension cost.

HOMER can optimize a system design for lowest life-cycle cost; Table 4.6 shows the optimization results for the full load and energyMalaya saving PV models. The power production is greater than the power requirements by 15.1% and 13.6% in the two models, respectively. Although this can beof better optimized by increasing the capacity of the battery, it is more practical to accept the augmented supply to accommodate reductions in the solar power over time.

Analysis of the PV/Wind-turbine full load model resulted in a power production of 765 kW per year, with 93% from the solar array and just 7% from the wind turbine. The initial capital cost was $4,888 and the total NPC was $6,898. Analysis of the PV/Wind- turbineUniversity energy saving model resulted in a power production of 835 kW per year. The power supply from wind turbine was also very small, just 6% compared to 94% from the solar array. The initial capital and NPC were $4,658 and $6,260, respectively. In both models, the wind turbine supplied only a small fraction of power, because the mean velocity of air was measured to be 1.9 m/s. These results are very poor compared to recommended working environmental conditions, which call for a mean wind velocity of 3 m/s (cut-in speed) or greater.

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Table 4.6 Optimization results of the PV configuration models based on full load and energy saving load.

Parameter Full load Energy Saving model model Total solar array production (kWh/year) 777 658

Total DC primary load (kWh/year) 529 456

Unmet electric load (%) 0.0 0.0

Maximum renewable penetration (%) 69.5 88.6

Mean solar electric output (kWh/day) 2.13 1.8

Excess solar electricity (%) 15.1 13.6

Solar penetration (%) 147 149

Hours of operation (hours/year) 4354 4354

Total number of battery (no.) 4 4 Bus voltage (V) Malaya24 24 Energy input to the batteries (kWh/year) 658 568

Energy output from the batteries (kWh/year)of 528 455

Expected life for the deep cycle batteries (year) 6.22 7.21

Cost of energy (COE) ($/kWh) 1.018 1.057

4.6 Acceptance Solar Energy Application

After collecting the results from 580 participants and analyzing them, it appears from the background information that the participants covered most Malaysian cities.

However,University 71% of participants were educated, while 29% were not. The ages of the participants are as follows: 63% were 18-25 years old, 35% were 26-40, while the rests were above 41. In fact, it was good to see that most participants were young people, as it is necessary to understand how the future is anticipated in terms of renewable energy and global issues for the entire world. The survey questions and answers are shown in

Appendix D (Part A and B).

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The survey started with very general questions about the level of interest regarding common issues on the environment and climate change. The purpose of these questions was to illustrate how people relate with these important issues besides show the participants the necessity for sustainable energy for a better environment. Hence, the first question was “Are you concerned about environmental issues?” The results show that approximately 93%, or the majority of respondents want to save the environment, while 4% are not interested about environment matters as they feel they may have no direct impact. Out of the respondents, 3% left this question unanswered.

The second question in the same context is “Have you heard of global warming/climate change? And do you know what it means for Malaysia as it has a long coastal line?”

According to the results, 95% of the people heard about climate change but 31% do not know what it means for a country with a long sea boundaryMalaya such as Malaysia. Overall, the results indicate the people in Malaysia are aware about environmental issues. But there is still a need for support programs toof demonstrate the potential hazards resulting from global warming with the irreversible effects from rising sea levels due to the rapid melting of polar ice caps that can reshape and also diminish all coastal regions of the world.

Regarding measuring the level of interest in renewable energy, question three was “Do you like renewable energy? If yes, what type of renewable energy do you like?” Here, differentUniversity opinions were presented, but 81% as the majority preferred solar energy application, as shown in Table 4.7. Along the same lines, the results of question four

“Which type of renewable energy is suitable for Malaysia?” show that 84% of the participants believe that solar energy is suitable for Malaysia, while only 6% feel that wind energy is suitable for Malaysia. Nuclear, geothermal and tidal energies received responses of 4%, 2% and 2% respectively, as shown in Table 4.8.

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Table 4.7 Results for interested in renewable energy in Malaysia.

Frequency Percent

Solar Energy 472 81%

Wind Energy 60 10%

Nuclear Energy 24 4%

Geothermal Energy 8 1%

Tidal Energy 13 2%

Don’t like 0 0%

No opinion 3 1%

Total 580 100%

Table 4.8 Results for type of renewable energy is suitable for Malaysia.

FrequencyMalaya Percent Solar Energy 487 84%

Wind Energy of36 6%

Nuclear Energy 25 4%

Geothermal Energy 13 2%

Tidal Energy 13 2%

No opinion 6 1%

Total 580 100%

TheUniversity fifth question in the survey was intended to test people’s background regarding renewable energy resources: “Do you think that solar radiation in Malaysia is promising for solar energy applications?” The results show that 63% of the participations believe that solar radiation is promising for solar applications, while 5% do not think so because of the partially cloudy days and continuous rain, which are characteristic of the weather in Malaysia. However, about 31% of the people had no opinion or were unsure about

110 solar resources. This reflects the fact that there is a need for resource studies regarding the potential of renewable energy resources in local settings.

The sixth survey question concerned the level of interest, specifically in photovoltaic

(PV) applications of solar renewable energy. The question was “Are you interested in solar PV applications?” The results signify that the level of interest in renewable energy is relatively moderate, with only about 51% of respondents interested in solar photovoltaic applications. Roughly 14% of the respondents were not interested, and

33% had no opinion. Following questionnaire collection, it was noted that the respondents were still confused about the usage of solar photovoltaic energy and some did not even know what solar PV is. Thus, we took some time to explain what photovoltaic means and what its purpose is. Hence, it can be concluded that although 81% liked the use of solar energy in Malaysia, Malaya 51% of them liked solar energy specifically. Besides 51% who liked solar energy, 35% people liked solar PV application in particular. of

This is clear from the results of question seven “Which type of solar application do you prefer or is implemented in your own home?” It appears that 35% of people liked photovoltaic electricity, as shown in Table 4.9.

Table 4.9 Results for type of solar application prefer in home.

Frequency Percent UniversityPhotovoltaic electricity 204 35% Solar-heated hot water 158 27%

Solar-powered space cooler 40 7%

Solar-heated pool 11 2%

Not any one 168 29%

Total 580 100%

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Question eight regards the contribution of solar energy awareness. Hence, we asked the following question: “If one (or more) solar energy project was carried out in your region, would it contribute to solar energy awareness?” The result show that 71%, or the majority of participants agree it would contribute to awareness, only 5% do not agree, while 24% have no opinion.

The ninth question was about the obstacles in starting to use solar PV as electricity generated in the participants’ own homes: “Would you like to start using photovoltaic- generated electricity in your home?” The questionnaire indicated that a ratio of about

36% of respondents were ready to use solar PV and these respondents had the potential to start solar PV application, but the problem was always that they did not have enough money to begin such project. 11% of respondents didMalaya not have enough space, and 37% did not have either money or space. This means there are still 36% of respondents in

Malaysia who have the potential to start of a PV application because they have enough space, but the problem is only with the capital cost of starting a solar PV project as shown in Table 4.10.

Table 4.10 Results of the willing to start photovoltaic-generated electricity in home.

Frequency Percent

Yes, but do not have enough money. 210 36% UniversityYes, but do not have enough space. 66 11% Yes, but have no money and no space. 215 37%

No, would not like. 89 15%

No answer 9 2%

Total 580 100%

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Table 4.10 additionally shows that the ratio of respondents who would not like to use solar PV in their own homes is 15%, which is very close to the ratio of respondents who were not interested in solar PV (14% from question six). Hence, based on the questionnaire results, we can conclude that the number of people who have the potential to invest in solar PV application is less than 36%, people who would not like to invest in PV application are less than 15% and the ratio of people who have a space problem is approximately 48%.

Survey questions 10, 11, 12 and 13 were developed to measure the respondents' knowledge about the Feed-in-Tariff, which was created by the government to enhance renewable energy application. Question 10 asked the participants: “Have you ever heard about the Feed-in-Tariff, and what does it mean?” Surprising results were obtained, where 70% of respondents have not heardMalaya about the Feed-in-Tariff program, and 15% knew about it but did not know whatof it meant. In the same context, question 11 in the survey was “If you have heard about the Feed- in-Tariff, do you think that the government’s incentives are enough to start photovoltaic-generated electricity in your home?” The results of this question show that although the percentage of respondents with knowledge about the Feed-in-Tariff program was only a mere 15% (question 10), survey question 11 showed that only 9% from those respondents believed that the government’s incentives were enough to start a projectUniversity on the photovoltaic-generated electricity in their homes. Meanwhile, 66% of people were unsure whether the government’s incentives were sufficient. This reflects that there is relative failure in the program or media responsible for enriching people’s knowledge about the government's program, especially the feed-in-tariff.

It was expected for some people to be unsure about the cost and payback period of solar

PV under the government’s feed-in-tariff incentives. Hence, we asked question 12: “Do

113 you know how much the capital cost of 1 kWp of grid-connected photovoltaic- generated electricity is?” and question 13 asked the following: “Do you know how many years the payback period of invested money in gird photovoltaic-generated electricity under the Feed-in-Tariff in your home town is?” The respondents were provided with some ranges for the capital cost and payback periods, such as less than

RM7500, between RM7500 and 12500, and more than RM12500 for capital cost in

Malaysian currency, and less than 6 years, between 6 and 10 years and more than 10 years for payback period.

The results of questions 12 and 13 were incredible, considering the respondents of the questionnaire represented Malaysian people. It can be fairly concluded that about 88% of Malaysian people do not know the range of the capital cost of grid-connected PV systems, while more or less the same amount of Malaya people, i.e. 86%, do not know the expected payback period as a result of investing in this domain. Hence, beside the capital cost of PV, the lack of knowledgeof regarding the government program render most Malaysians uninterested about renewable energy programs and unwilling to invest in such programs. It is worth mentioning that information such as capital cost and payback period are potentially critical to beneficiaries (e.g. designers, investors, financiers, and home owners) who are ever so willing to implement any investment plan in the field of procuring alternative sustainable energies through the use of the solar PV package.University At the end of the questionnaire, question 14 was developed to sophisticatedly test the respondents’ answers about sharing the benefits of selling electricity to the grid with any bank under the Feed-in-Tariff. To obtain a loan from the bank to start a PV project, there is one condition: first and foremost, the loan must be covered by selling electricity over a period of 5 to 8 years, after which the bank will share the benefits of all residual periods. The results indicate that 42% of the respondents agreed with this suggestion.

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This number is considered quite good because only 16% disagreed, and 42% of respondents had no opinion. Therefore, it is highly recommend that the government or any private bank look into the feasibility of this suggestion as there are many people who rationally agree with this suggestion.

Malaya of

University

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CHAPTER 5: CONCLUSIONS

5.1 Introduction

The present work represents a qualitative and quantitative study seeking to enhance renewable-energy application in Peninsular Malaysia and fulfilling the energy target requirement in Malaysian plans. This embodied by modeling solar radiation resources, and grid-connected applications, which are potentially critical to people who are willing to make any investment plans in the field of solar energy. However, there remains a need to highlights the obstacles preventing the adoption of solar PV technology under government incentives. The fundamental conclusions of the thesis are presented in the following subsections.

5.2 Solar Radiation Assessment Using ANN The Artificial Neural Network (ANN) has been usedMalaya for solar radiation modeling, as it is capable of dealing with non-linear parametersof through solar radiation modeling, such as complex topographical attributes and limited number of meteorological stations.

These key parameters have created some difficulties for researchers to assess solar resources, which led to deprive Peninsular Malaysia from reliable solar radiation maps.

Investigations carried out in the current study demonstrate that artificial neural networks are an accurate tool for solar radiation prediction in locations with complex topographical attributes including high altitudes. By using five input parameters, namelyUniversity longitude, latitude, altitude, extraterrestrial solar radiation and months during the year, it has been found that the Mean Absolute Percentage Error (MAPE) is less than 6.07% for all designed models, inferring a high level of reliability in terms of weather data prediction. The model with a MAPE value of 4.04% and correlation factor

(R) of 0.965 attained fairly steady solar radiation prediction. Following data prediction using the ANN model (Appendix A1-A12), ArcGIS was used to display solar energy locations that are potentially critical to designers, investors, financiers, and home

116 owners, to name a few. The predicted solar radiation maps shows that Peninsular

Malaysia receives a monthly average of daily solar radiation ranging from 3.82 to 5.23 kWh/m2/day throughout the year. The highest solar radiation is estimated to be between

5.79 and 5.92 kWh/m2/day in February, March and April, while the lowest is estimated to be between 3.33 and 3.40 kWh/m2/day in November and December, respectively.

The northeast region of Peninsular Malaysia has the highest solar energy potential throughout the year compared with other locations, especially the extreme, western coastal areas. A comparison between the annual solar radiation map and terrain map obviously shows that the mountain range follows the solar radiation map pattern, which is comparatively logical. Hence, the ANN model seems generally quite promising for the development of solar radiation maps in locations with insufficient meteorological stations to support data collection. Malaya 5.3 Grid-Connected PV system Assessment

Two pragmatic PV applications have madeof a contribution, with the first being the grid- connected PV system and the second, standalone hybrid street lighting. Direct analysis was used for modeling the grid-connected solar PV with several experimental works performed to enhance the results. In this thesis, various issues were highlighted, such as the Feed-in-Tariff program, payback period and unit cost of energy. Based on the energy generated by a solar PV system, the solar PV renewable energy analysis conducted on the prospective locations shows a minimum value of 1.01 MWh/kWp in theUniversity town of Cameron Highlands and a maximum value of 1.31 MWh/kWp in the town of Lubok Merbau; the corresponding capacity factors are 11.5% and 15% respectively.

The solar energy production in Kuala Lumpur is 1.15 MWh/kWp/year, the number of operation hours are up to 4,328 hours/year whilst the maximum output power is 0.67 kW/kWp. Based on the electricity cost of grid-connected solar PV stations, the financial analysis shows that Cameron Highlands has the highest energy production cost of about

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$0.36/kWh, while towns in north Peninsular Malaysia, such as Chuping, Alor Setar,

Lubok Merbau and Penang have the lowest production cost of around $0.28/kWh. The average cost of energy in Peninsular Malaysia is $0.30/kWh. In Kuala Lumpur the cost of PV electricity with capacity below 10 kW ranges between a maximum value of

$0.38/kWh and minimum of $0.24/kWh, and has a mean cost of about $0.31/kWh.

Based on the Feed-in-Tariff program analysis, the normal payback period of the grid- connected solar PV system in Peninsular Malaysia without incentives would be between

23 and 37 years, but after the Feed-in-Tariff incentive the average payback period based on the average solar PV price would range from a minimum of 7.4 years in Beyan

Lepas to a maximum of 9.6 years in Cameron Highlands, while the mean payback period in Peninsular Malaysia would be 8 years. The total sum of these values reflects the fact that government incentives would reduce theMalaya payback period by approximately 73% or more and would result in much more favorable economies for owners. Hence, the solar PV system becomes economicallyof viable under the current conditions in Malaysia mainly due to the Feed-in-Tariff program. Based on the environmental analysis and the potential CO2 abatement, the average GHG emission abatement due to the use of grid connected solar PV in Peninsular Malaysia is found to be 0.90871 tCO2/kWp/year, which means that if the PV provides around 0.01% of global electricity production in Malaysia, which is 123,561GWh based on 2011, the resultant effect will be the decrease of approximately 9.3 Mega tonnes of CO2 emissions per year wherein theseUniversity emissions will not pose any health hazard in the local atmosphere.

5.4 Sensitivity Analysis of PV parameters

The sensitivity analysis was performed using HOMER to study the impact of different parameters on energy analysis and system cost. According to the weather parameters, including wind speed, the analyses on the PV power model do not result in significant energy calculation error. However, in terms of system capacity, the sensitivity analyses

118 on increasing system size show that a capacity from 1 MW to 10 MW has an LCOE value of about $0.219/kWh, which is rather competitive compared with $0.307/kWh for a system with capacity of 10 kW. Based on the location parameters, the sensitivity of ground reflectance has a modest effect on LCOE, where by changing the values of ground reflectance from 0.2 to 0.7 the LCOE can be increased by not more than 0.25%.

However, sensitivity analyses on the PV mounted system indicate that any pitch angle augmented from zero (0) to 7/12 for all PV azimuth angles can increase the LCOE by

9% to 10%; for a vertical surface, the LCOE can be increased by 171% more than horizontal surface if it solely depends on the azimuth angle of the surface. Regarding the PV parameters, the temperature coefficient was found to be (7.7%), between -

0.6%/°C and -0.2%/°C respectively, while sensitivity analyses on annual interest rates indicate considerable impact, where by decreasing theMalaya interest rate from 6% to 4% the LCOE can be decreased by up to 15.6%.

5.5 Standalone Street Light Assessmentof

The feasibility study of renewable energy powering Standalone Street Light can further enhance the utilization of local renewable energy resources to contribute and cover the supply and demand gap in the national electricity production and to perform better in the field of socio-economic development. In the respective analysis, the study was done based on the selected state with the highest energy resources among all other states.

Hence, if the technical and economic analyses for the system reflect the reliability of the applications,University then the case study can be extended to other locations; while if the system is not reliable, it will reflect the fact that using this application in other states will not be viable.

The analyses on the Standalone Street Light show that illuminating streets by SPSL is feasible in remote areas as compared with the high costs required to extend the grid to reach these locations. The cost analyses show that the PV full load model of SPSL have

119 a total net present cost (NPC) of $6,177 and cost of energy (COE) of $1.018/kWh, while the energy saving model of the SPSL had a NPC of $5,531 and COE of

$1.057/kWh. In general, our analyses indicate that using the renewable energy based solar radiation to power street lighting is not recommended in locations has a grid in

Malaysia, this is because such application depends totally on the time-limited energy storage capacity and the saving capacity in the batteries during the morning and spending this energy for the rest of the night, while in Malaysia during the day, it is quite normal during certain months that the weather can be continuously cloudy accompanied with heavy rains as these phenomena have been amply demonstrated through the analyses of the hourly solar radiation database. Thus, if we are to overcome these problems then it will be essentially necessary to increase the size of the PV plants and the number of batteries just to harvest and storeMalaya the required energy in a shorter period but at the expense of increase in size and cost. But if serious security threat is prevalent in the area (such as an increase inof the level of crime), then the security of the area will far outweigh the cost of electrical energy as it all entirely depends on what priority one is looking at.

5.6 Social Acceptance of Renewable Energy

As the main purpose of the work is to enhance renewable energy (RE) application in

Peninsular Malaysia, the survey was an integral part and from which the suggestions obtained. Meanwhile, highlight people’s obstacles regarding to adopt RE are expected to University strengthen any future policy and regulatory framework, and encourage the widespread adoption of RE projects as well as the implementation of solar-energy usage globally. The relevant surveys conducted repeatedly produced a clear indication that most people in Malaysia are still not truly educated as yet in terms of awareness of the need to harness renewable energy through solar-radiation, or are not fully aware of the promising use of electricity obtained through the photovoltaic mechanisms. But there

120 are 2 sides to the same coin in any comparison and to be more justifiably specific, this is not only because of the relative failure in education and training programs but it can also be partly attributed to the wide economic gap between the haves and have-nots in the multiracial society in Malaysia -- which represents a similar situation to other developing countries without exception. Most people doesn’t have a visibility study regarding the benefits of the project, while others, around 36%, need the fiscal support to start a PV project, through a capital cost loan, even if the loan stipulates sharing the benefits in advance.

Based on the current scenario, the government of Malaysia still presents a difficulty despite the good incentives under the Feed-in-Tariff program to enhance and accelerate solar photovoltaic system application. These incentives mean that those who possess the capital cost for a PV system with the available incentivesMalaya will gain in the payback period reduction. On the other hand, these incentives will not make sense for people who have the available space for a PV projectof but not enough money to start and who may also not be able to wait the long payback period to receive the benefits, such as those living a rustic life in remote, rural areas. Therefore, a better suggestion for the government of Malaysia is to provide subsidies to those who have the space but not the money. That subsidy can be from any bank that shares the payback period with the people and likewise, the same subsidy would also enable people to participate in the Feed-Universityin-Tariff program even if they do not have enough money. The facts mentioned above comprise the first challenge that willing people are squarely faced with, while the second challenge is the quality of solar PV products under the

Feed-in-Tariff program. It is also relevant to consider from the proper perspective that a market using the cheapest technology cannot keep up production of the same quality and amount of energy requirement for the next 21 years. Therefore, it is highly recommended that people insist on high quality projects when they decide to go through

121 the Feed-in-Tariff program. However, solar PV companies must also be obligated to state the warrantee period of power production because higher accuracy is needed for power production from solar PV for any future analyses. This process will make solar

PV investors more confident in their enormous investments.

The third challenge is the degradation of annual Feed-in-Tariff incentives, wherein the government should stall this degradation until success of informative promotional features has been visibly achieved and prominently displayed in the printed and electronic media by the government and associated commercial sectors. However, people in Malaysia should bear in mind that there is no guarantee electricity prices will be same 21 years from now due to increasing energy demand that can also cause an energy crisis, along with the Malaysian vision to become a successful developed country by 2020. For the moment, the Feed-in-TariffMalaya represents the better available solution. of Last but not least, the present dissertation has essentially highlighted and vehemently emphasized the advantages of using PV plants for an eventual global method of harnessing renewable energy via solar radiation conversion. Nonetheless, it is of utmost importance in all fairness to take an adequate perspective to seriously reflect on the irreversible effects from rising sea levels due to the rapid melting of the polar ice caps.

These irreversible effects can reshape and decrease all coastal regions worldwide, not to mentionUniversity the resultant massive flooding of food-producing, low-lying coastal areas and the depletion of food sources both on land and sea. The massive CO2 content in the atmosphere is responsible for these drastic effects, especially in Malaysia, which has a long coast line. As a matter of fact, we will be living in a world full of uncertainties as the list mentioned above presents merely a few potential disruptions, and ultimately, a point will be reached when the earth will no longer be habitable. If our prognostic prediction is not too far out, there is still only half a century to mend our ways before it

122 is too late. At the same time, comparing the production costs of renewable solar radiation energy and other sources should not arise at all. Despite the cost, we must bear without flinching, because it basically entails redeeming our very own global survival in the near future and the next generations.

5.7 Recommendation for future work

Studies of the other applications such as feasibility of biomass renewable energy and government incentives on biomass, and make a comparison between this application and our study. However, I recommend studying PV hydrogen systems for remote area.

This is because a study by the World Bank on rural electrification programs placed the average cost of grid extension (per km) between $5,000-$10,000, rising to $19,000-

22,000 in difficult terrains. This means PV hydrogen system could be potential for rural area even if it is high cost. However, as the worldMalaya is going to use a hydrogen car for sustainable development, I recommend studying the feasibility of PV for hydrogen production in petrol stations. This is becauseof in petrol station, there is a lot of useless area and underground area can be used for hydrogen storage in terms of safety. The comprehensive assessment of the above project will facilitate an effective demonstration of the techno-economic viability, design, development, financing and sustainable operation of grid-connected power generation and standalone generation projects. This is important to foster the applications of renewable energy in Peninsular

Malaysia to cut down electricity cost, and reduce the pollution levels and global warmingUniversity resulting from the use of conventional energy sources. Last but not least, there is a need to conduct survey every five years at least with new questions in order to track the progress regarding peoples’ knowledge about the government programs that deal with renewable energy.

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APPENDICES

Appendix A: Predicted Solar Radiation

Table A1 Solar radiation and clearness index prediction for month January.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication 1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.56 5.04 2 41636 TAMAN NEGERI PERLIS 100.2000 6.7000 0 0.57 5.03 PERLIS 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.58 5.19 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.58 5.23 5 41543 HOSPITAL SUNGAI KEDAH 100.5000 5.6500 8 0.59 5.23 PETANI 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.56 5.04 7 41612 P PEMBIAKAN IKAN KEDAH 100.4667 6.2500 10 0.57 5.09 JITRA 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.56 4.96 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.56 4.96 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.58 5.15 11 41548 P. PERT. CHAROK KEDAH 100.7167 5.8000 31 0.57 5.11 PADANG 12 41549 PUSAT PERT.BATU KEDAH 100.8000 5.9667 71 0.56 5.02 SEKETOL 13 41551 MADA KOTA SARANG KEDAH 100.3833 5.9667 5 0.58 5.20 SEMUT, PENDANG 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950Malaya 5.9914 38 0.57 5.11 15 41603 LAMAN PADI KEDAH 99.7225 6.2978 1 0.60 5.33 LANGKAWI 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.60 5.27 17 41606 P. PERT. TELUK KEDAH of100.1803 6.0975 1 0.59 5.24 CHENGAI 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.58 5.17 19 41611 PERTANIAN GAJAH KEDAH 100.5333 6.1667 15 0.57 5.09 MATI 20 41613 FELDA BUKIT KEDAH 100.5356 6.2111 92 0.57 5.05 TEMBAGA 21 41615 MADA TELAGA BATU, KEDAH 100.3500 6.2333 5 0.58 5.13 JITRA 22 41622 FELDA BATU LAPAN KEDAH 100.5367 6.4167 0 0.57 5.01 23 41624 FELDA BUKIT KEDAH 100.4747 6.4906 0 0.57 5.00 TANGGA 24 41625 MARDI BUKIT KEDAH 100.5000 6.4667 50 0.57 5.00 TANGGA 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.60 5.33 26 41533 PUSAT KES. BKT. P. PINANG 100.2667 5.4167 732 0.55 4.89 BENDERA 27 41536 KEBUN BUNGA PULAU P. PINANG 100.2833 5.4333 74 0.59 5.29 PINANG 28 University41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.59 5.25 29 43402 HOSPITAL TELUK PERAK 101.0167 4.0333 3 0.51 4.65 INTAN 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.52 4.68 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.52 4.68 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.53 4.77 33 43417 HOSPITAL BATU PERAK 101.0333 4.4667 34 0.54 4.90 GAJAH 34 43418 SEISMO. STN BUKIT PERAK 101.0167 4.5833 240 0.53 4.81 KLEDANG 35 43419 HOSPITAL BAHAGIA PERAK 101.1667 4.6667 70 0.54 4.92 ULU KINTA 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.52 4.66

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37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.54 4.91 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.47 4.28 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.57 5.14 40 43447 HOSPITAL KUALA PERAK 100.9333 4.7667 68 0.56 5.03 KANGSAR 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.58 5.23 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.56 5.06 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.55 4.90 44 43507 POS POI PERAK 101.2167 5.1000 1524 0.46 4.09 45 43508 STN. JANALETRIK PERAK 101.2000 5.3000 110 0.55 4.97 BERSIA 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.54 4.82 47 43520 HOSPITAL PARIT PERAK 100.5000 5.1333 3 0.58 5.26 BUNTAR 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.55 4.92 49 43562 RPS AYER BANUN PERAK 101.3500 5.5333 200 0.54 4.85 GRIK 50 43333 FELDA NENERING PERAK 101.0667 5.6833 323 0.55 4.88 DUA 51 43347 FELDA SUNGAI PERAK 101.0333 3.7833 0 0.49 4.45 BEHRANG 52 43353 PUSAT PERT. TITI PERAK 100.8500 4.3667 0 0.54 4.91 GANTONG 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.51 4.60 54 43357 FELDA TROLAK PERAK 101.3667 3.9500 58 0.49 4.46 UTARA 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.51 4.62 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.51 4.64 57 43404 PERTANIAN LEKIR PERAK 100.7467Malaya 4.1386 7 0.52 4.77 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.54 4.93 59 43426 MARDI HILIR PERAK PERAK 100.8667 3.8833 9 0.50 4.54 (LKM) 60 43448 MARDI KUALA PERAK of100.9072 4.7639 66 0.56 5.04 KANGSAR 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.56 5.06 62 43500 CHERSONESE ESTATE PERAK 101.4333 5.0000 94 0.54 4.87 KUALA KURAU 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.53 4.76 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.46 4.28 65 44252 AMPANGAN AIR SG. SELANGOR 101.8667 2.9333 0 0.46 4.25 SEMENYIH 66 44258 RUMAH API ONE SELANGOR 101.0000 2.8833 21 0.43 3.98 FATHOM BANK 67 44298 MARINE DEPT. PORT SELANGOR 101.3833 3.0167 2 0.45 4.12 KLANG 68 44305 NEB CONNAUGHT SELANGOR 101.4667 3.0500 3 0.45 4.15 BRIDGE POWER STATION KLANG 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.45 4.10 70 44320 AMPANGAN ULU SELANGOR 101.8667 3.2167 233 0.43 3.99 UniversityLANGAT 71 44322 HOSPITAL KUALA SELANGOR 101.6500 3.5667 61 0.46 4.22 KUBU BARU 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.46 4.19 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.47 4.30 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.45 4.19 75 44255 PUSAT PERT. TELOK SELANGOR 101.5158 2.8167 0 0.45 4.17 DATOK 76 44256 BANTING OIL PALM SELANGOR 101.5000 2.8167 0 0.45 4.17 RES. STN 77 44301 PUSAT PERT. SELANGOR 101.7000 3.0000 44 0.45 4.16 SERDANG 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.45 4.16 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.44 4.08

125

80 44315 PUSAT PERTANIAN SELANGOR 101.6408 3.4797 46 0.46 4.19 BATANG KALI 81 44325 MARDI TANJONG SELANGOR 101.1564 3.4547 2 0.46 4.24 KARANG 82 44326 PUSAT PERT.TJG. SELANGOR 101.1833 3.4167 0 0.46 4.21 KARANG 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.47 4.27 84 44347 P.LAT.PERT. SELANGOR 101.4833 3.6333 87 0.46 4.23 KALUMPANG 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.45 4.14 86 44356 TENNAMARAN SELANGOR 101.4167 3.4000 15 0.46 4.19 ESTATE 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.45 4.15 88 44361 SUNGAI BULUH SELANGOR 101.3167 3.3000 0 0.45 4.17 ESTATE 89 44362 SELANGOR RIVER SELANGOR 101.3333 3.3500 0 0.46 4.20 ESTATE 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.46 4.22 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.46 4.19 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.45 4.14 93 48307 UNIVERSITY MALAYA K. LUMPUR 101.6500 3.1167 104.0 0.44 4.06 KUALA LUMPUR 95 45224 RUMAH API TANJONG. N. SEMBILAN 101.8500 2.4000 0 0.48 4.47 TUAN 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.46 4.28 97 45244 HOSPITAL KUALA N. SEMBILAN 102.2500 2.7333 107 0.46 4.26 PILAH 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.45 4.11 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.47 4.36 100 45231 PUSAT PERTANIAN N. SEMBILAN 102.3833 2.5167 70 0.48 4.48 GEMENCHEH Malaya 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.48 4.49 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.48 4.45 103 45237 HAIWAN JELAI GEMAS N. SEMBILANof 102.5458 2.6289 46 0.48 4.44 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.48 4.43 105 45242 FELDA BUKIT ROKAN N. SEMBILAN 102.4167 2.6667 0 0.48 4.48 UTARA 106 45245 LADANG BATANG N. SEMBILAN 102.4667 2.7000 67 0.47 4.38 JELAI ROMPIN 107 45251 CHEMARA RES. N. SEMBILAN 101.7875 2.6456 5 0.47 4.33 TANAH MERAH 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.46 4.29 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.47 4.31 110 45256 LADANG AYER HITAM N. SEMBILAN 102.4167 2.9333 59 0.46 4.24 (GLANDALE) 111 46203 RUMAH API PULAU MELAKA 102.3333 2.0500 53 0.49 4.57 UNDAN 112 46219 ALAM (AKEDEMI MELAKA 102.0500 2.3667 0 0.49 4.56 LAUT MALAYSIA) 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.50 4.69 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.50 4.62 115 University46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.50 4.67 116 46211 CHEMAR RES. MELAKA 102.4167 2.3228 27 0.50 4.63 SERKAM JASIN 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.49 4.54 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.49 4.52 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.49 4.58 120 46225 FELDA HUTAN MELAKA 102.3000 2.3667 0 0.50 4.62 PERCHA 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.48 4.32 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.51 4.60 123 40432 RPS KUALA BETIS KELANTAN 101.7500 4.7000 153 0.51 4.64 (LAMBOK) 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.48 4.30

126

125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.50 4.50 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.51 4.56 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.52 4.71 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.52 4.68 129 40512 POS WIAS (KUALA KELANTAN 101.8167 5.1167 76 0.53 4.73 WOOD) 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.51 4.61 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.53 4.70 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.51 4.56 133 40546 P. TER. HAIWAN KELANTAN 102.0092 5.8111 0 0.52 4.66 TANAH MERAH 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.51 4.59 135 40586 PEJ.HAI. JAJAHAN KELANTAN 102.2000 5.7667 31 0.52 4.62 MACHANG 136 40587 P. PERT. BATANG KELANTAN 102.0500 5.8167 21 0.52 4.65 MERBAU 137 40588 PUSAT PERTANIAN KELANTAN 102.3000 5.9667 7 0.52 4.60 MELOR 138 40660 PUSAT PERTANIAN KELANTAN 102.4000 6.0500 3 0.52 4.58 BACHOK 139 40663 PUSAT. PERT. PASIR KELANTAN 102.1167 6.0333 9 0.52 4.64 MAS 140 40664 PUSAT PERT. KELANTAN 102.2658 6.1044 6 0.52 4.61 LUNDANG 141 40666 MARDI KUBANG KELANTAN 102.2808 6.0833 8 0.52 4.61 KERANJI 142 49411 BALAI PENGHULU TERENGGANU 103.4333 4.5167 0 0.47 4.23 KERTIH 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.48 4.33 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167Malaya 4.2333 3 0.45 4.13 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.48 4.31 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.45 4.09 147 49454 FELDA JERANGAU TERENGGANUof 103.1833 4.9000 0 0.48 4.34 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.45 4.11 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.46 4.14 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.45 4.13 151 49480 LADANG KOKO TERENGGANU 103.1833 4.9333 0 0.48 4.35 LANDAS JERANGAU 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.48 4.35 153 49503 PERTANIAN PADANG TERENGGANU 103.0167 5.0500 0 0.49 4.38 IPOH 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.48 4.36 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.49 4.37 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.49 4.43 157 49539 INSTITUT PERTANIAN TERENGGANU 102.6167 5.6500 10 0.51 4.53 BESUT 158 42249 JHEOA KUALA PAHANG 103.5000 2.8167 0 0.46 4.22 ROMPIN 159 University42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.46 4.24 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.46 4.25 161 42304 PUSAT LATIHAN PAHANG 102.9167 3.6000 0 0.44 4.07 JHEOA Km 56 (Bt. 34) 162 42318 PERBADANAN PAHANG 101.7333 3.7167 1280 0.43 3.91 KEMAJUAN BKT. FRASER 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.45 4.15 164 42321 TALIKOM BUKIT PAHANG 101.7500 3.7167 1324 0.43 3.92 PENINJAU (FRASER HILL) 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.45 4.16 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.44 4.05 167 42325 RPS BKT. SEROK PAHANG 102.8333 2.9167 40 0.46 4.24 ROMPIN

127

168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.50 4.50 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.46 4.23 170 42332 P PERIKANAN BKT. PAHANG 101.8167 3.3667 153 0.44 4.08 TINGGI 171 42337 TAPAK SEMAIAN PAHANG 101.8867 3.3900 153 0.44 4.07 LENTANG, BENTONG, PAHANG 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.46 4.25 173 42378 SEK. MEN. AHMAD PAHANG 103.3333 3.4833 4 0.43 3.97 PEKAN 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.48 4.33 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.47 4.25 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.53 4.81 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.49 4.45 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.50 4.57 179 42248 PST. PERTANIAN PAHANG 103.5833 2.7167 0 0.46 4.27 HORTIKULTOR JANGLAU 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.47 4.30 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.46 4.19 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.45 4.14 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.46 4.18 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.44 4.03 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.45 4.14 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.45 4.11 187 42330 FELDA KAMPUNG PAHANG 102.0333 3.5000 70 0.45 4.15 SERTIK Malaya 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.46 4.18 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.45 4.08 190 42335 PUSAT PERT. GALI PAHANG 101.8500 3.7833 159 0.46 4.20 RAUB of 191 42336 FELDA KAMPONG PAHANG 102.5000 3.5833 40 0.45 4.13 AWAH 192 42338 PUSAT PERTANIAN PAHANG 102.1500 3.4667 0 0.46 4.22 JAMBU RIAS 193 42339 FELDA KAMPONG PAHANG 102.8528 3.6453 0 0.45 4.10 NEW ZEALAND 194 42340 FELDA JENDERAK PAHANG 102.3000 3.6833 0 0.46 4.22 SELATAN 195 42341 FELDA JENDERAK PAHANG 102.3119 3.6786 46 0.46 4.17 UTARA 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.45 4.17 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.47 4.28 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.46 4.18 199 42349 FELDA SUNGAI PAHANG 103.1667 3.8000 50 0.44 4.01 PANCING SELATAN 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.45 4.15 201 42354 FELDA BUKIT PAHANG 103.1500 3.9167 0 0.45 4.08 UniversityKUANTAN 202 42360 P.P.TANAMAN PAHANG 102.5000 3.5833 31 0.45 4.13 KAMPUNG AWAH 203 42363 FELDA PPP TUN PAHANG 102.5667 3.8333 76 0.45 4.15 RAZAK 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.45 4.09 206 42381 PUSAT PERTANIAN PAHANG 103.0833 3.8333 31 0.44 4.06 BUKIT GOH 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.48 4.35 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.47 4.32 209 42415 LADANG FELDA PAHANG 102.1667 4.2500 0 0.49 4.45 KECHAU 06 210 42416 FELDA SUNGAI PAHANG 101.8667 4.2667 0 0.50 4.56 KOYAN SATU 211 42421 MARDI CAMERON PAHANG 101.3872 4.4681 1448 0.44 4.03 HIGHLANDS 128

212 42464 MARDI SUNGAI PAHANG 103.3833 4.0667 4 0.45 4.08 BAGING 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.46 4.30 214 47117 HOSPITAL JOHOR JOHORE 103.7500 1.4667 15 0.45 4.25 BARU 215 47120 HOSPITAL KOTA JOHORE 103.9000 1.7333 9 0.47 4.43 TINGGI 216 47121 JKR BANDAR KOTA JOHORE 103.9000 1.7333 33 0.47 4.40 TINGGI 217 47139 RUMAH API BKT. JOHORE 102.8833 1.8000 77 0.48 4.49 SEGENTING 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.48 4.50 219 47202 PUSAT BUKIT PASIR JOHORE 102.6333 2.1000 11 0.50 4.69 MUAR 220 47203 KLINIK KESIHATAN JOHORE 103.5333 2.2167 53 0.49 4.54 KAHANG 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.45 4.22 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.49 4.54 223 47207 PEJ. KEJORA BANDAR JOHORE 104.2333 1.5500 35 0.45 4.24 PENAWAR 224 47214 MAJLIS DAERAH JOHORE 103.0333 2.3833 40 0.49 4.58 LABIS 225 47116 MARDI ALOR BUKIT JOHORE 103.4500 1.5000 3 0.46 4.32 PONTIAN 226 47118 PUSAT PERT.KONG JOHORE 103.8833 1.4833 38 0.45 4.23 KONG 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.48 4.46 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.46 4.32 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.46 4.33 230 47130 PUSAT PERT. PARIT JOHORE 103.0833 1.7167 2 0.48 4.52 BOTAK 231 47134 C. RES. LAYANG JOHORE 103.4678Malaya 1.8406 31 0.48 4.53 LAYANG 232 47137 KULIM MONTEL FARM JOHORE 103.9167 1.6333 39 0.46 4.33 (47136) (LADANG BASIR ISMAIL) 233 47140 P. PERT. SG. SUDAH JOHORE of102.7167 1.9000 2 0.50 4.64 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.48 4.48 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.47 4.41 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.47 4.42 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.48 4.46 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.49 4.57 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.48 4.48 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.50 4.64 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.50 4.64 242 47209 FELDA TENGGAROH JOHORE 103.8667 2.0833 25 0.48 4.49 TIMUR 2 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.50 4.66 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.50 4.63 245 University47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.50 4.64 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.50 4.64 247 47220 FELDA BUKIT JOHORE 102.8000 2.3333 0 0.50 4.66 SERAMPANG 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.50 4.65 249 47229 FELCRA TANAH JOHORE 103.6489 2.4753 0 0.48 4.44 ABANG 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.49 4.50 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.49 4.53 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.47 4.32

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Table A2 Solar radiation and clearness index prediction for month February.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.60 5.63 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.60 5.61 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.60 5.66 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.60 5.67 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.60 5.70 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.60 5.65 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.60 5.67 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.60 5.62 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.59 5.61 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.60 5.67 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.60 5.67 PUSAT PERT.BATU 0.60 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 5.65 MADA KOTA SARANG 0.60 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 5.71 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.60 5.67 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.61 5.72 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.60 5.69 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.61 5.71 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.60 5.69 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.60 5.67 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.60 5.64 MADA TELAGA BATU, 0.60 21 41615 JITRA KEDAH 100.3500 6.2333 5 5.68 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.60 5.64 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.60 5.63 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.60 5.62 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.60 5.71 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.54 5.17 KEBUN BUNGA PULAU 0.60 27 41536 PINANG P. PINANG 100.2833 5.4333 74 5.68 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.60 5.70 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.53 5.07 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.54 5.18 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.54 5.18 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.55 5.23 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.56 5.33 SEISMO. STN BUKIT 0.54 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 5.20 HOSPITAL BAHAGIA ULU 0.57 35 43419 KINTA PERAK 101.1667 4.6667 70 5.39 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.54 5.11 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.57 5.44 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.49 4.62 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.58 5.53 HOSPITAL KUALA 0.57 40 43447 KANGSAR PERAK 100.9333 4.7667 68 5.45 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.59 5.61 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.58 5.54 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.57 5.47 44 43507 POS POI PERAK 101.2167 5.1000 1524 0.47 4.50

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45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.59 5.56 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.57 5.43 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.59 5.64 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.58 5.52 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.58 5.53 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.58 5.51 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.51 4.91 PUSAT PERT. TITI 0.55 52 43353 GANTONG PERAK 100.8500 4.3667 0 5.28 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.52 5.02 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.52 5.02 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.52 5.03 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.53 5.06 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.53 5.10 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.56 5.33 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.51 4.92 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.57 5.45 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.58 5.55 CHERSONESE ESTATE 0.58 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 5.49 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.58 5.54 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.52 5.00 AMPANGAN AIR SG. 0.52 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 5.01 RUMAH API ONE FATHOM Malaya0.46 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.42 MARINE DEPT. PORT 0.49 67 44298 KLANG SELANGOR 101.3833 3.0167 2 4.77 NEB CONNAUGHT BRIDGE 0.50 68 44305 POWER STATION KLANG SELANGORof 101.4667 3.0500 3 4.83 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.50 4.85 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.49 4.74 HOSPITAL KUALA KUBU 0.51 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.93 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.51 4.96 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.51 4.98 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.51 4.94 PUSAT PERT. TELOK 0.50 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.85 BANTING OIL PALM RES. 0.50 76 44256 STN SELANGOR 101.5000 2.8167 0 4.84 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.51 4.90 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.50 4.83 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.50 4.81 PUSAT PERTANIAN 0.51 80 University44315 BATANG KALI SELANGOR 101.6408 3.4797 46 4.93 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.50 4.77 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.49 4.76 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.53 5.07 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.51 4.89 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.50 4.79 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.50 4.85 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.50 4.81 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.50 4.79 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.50 4.82 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.50 4.83

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91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.51 4.93 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.51 4.90 UNIVERSITY MALAYA 0.50 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.81 RUMAH API TANJONG. 0.52 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 5.07 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.52 5.02 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.52 5.06 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.51 4.93 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.52 5.06 PUSAT PERTANIAN 0.53 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 5.17 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.53 5.18 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.54 5.19 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.54 5.18 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.54 5.19 FELDA BUKIT ROKAN 0.54 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 5.20 LADANG BATANG JELAI 0.53 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 5.14 CHEMARA RES. TANAH 0.52 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 5.02 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.52 5.01 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.52 5.04 LADANG AYER HITAM 0.52 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 5.06 111 46203 RUMAH API PULAU UNDAN MELAKA 102.3333 2.0500 53 0.52 5.10 ALAM (AKEDEMI LAUT Malaya0.53 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 5.15 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.54 5.21 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.53 5.19 of 0.54 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 5.24 CHEMAR RES. SERKAM 0.54 116 46211 JASIN MELAKA 102.4167 2.3228 27 5.22 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.53 5.19 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.53 5.13 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.54 5.22 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.54 5.22 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.50 4.77 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.54 5.19 RPS KUALA BETIS 0.56 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 5.31 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.50 4.79 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.57 5.41 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.54 5.12 127 University40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.56 5.33 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.57 5.39 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.58 5.50 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.55 5.26 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.58 5.53 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.57 5.45 P. TER. HAIWAN TANAH 0.59 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.54 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.58 5.48 PEJ.HAI. JAJAHAN 0.58 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 5.50 136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.58 5.52

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137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.58 5.47 PUSAT PERTANIAN 0.58 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.43 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.58 5.48 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.58 5.43 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.58 5.44 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.52 5.01 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.55 5.24 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.50 4.83 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.54 5.15 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.50 4.81 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.56 5.32 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.51 4.89 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.51 4.93 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.51 4.87 LADANG KOKO LANDAS 0.56 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 5.32 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.56 5.34 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.57 5.39 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.56 5.33 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.57 5.38 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.57 5.42 INSTITUT PERTANIAN 0.58 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 5.47 Malaya0.51 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 4.94 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.52 5.06 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.52 5.00 PUSAT LATIHAN JHEOA Km of 0.51 161 42304 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.88 PERBADANAN KEMAJUAN 0.43 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 4.14 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.51 4.93 TALIKOM BUKIT PENINJAU 0.43 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 4.14 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.52 4.99 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.49 4.77 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.52 5.05 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.53 5.12 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.52 5.04 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.50 4.83 TAPAK SEMAIAN LENTANG, BENTONG, 0.50 171 42337 PAHANG PAHANG 101.8867 3.3900 153 4.85 172 University42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.53 5.09 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.48 4.64 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.53 5.06 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.49 4.73 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.56 5.39 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.52 4.97 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.54 5.20 PST. PERTANIAN 0.52 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 5.00 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.53 5.10 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.52 5.05 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.52 4.99

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183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.52 5.04 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.50 4.80 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.51 4.91 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.50 4.87 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.52 4.96 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.52 4.97 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.51 4.91 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.51 4.93 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.52 4.98 PUSAT PERTANIAN JAMBU 0.52 192 42338 RIAS PAHANG 102.1500 3.4667 0 5.05 FELDA KAMPONG NEW 0.51 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.91 FELDA JENDERAK 0.53 194 42340 SELATAN PAHANG 102.3000 3.6833 0 5.07 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.52 5.02 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.51 4.96 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.53 5.06 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.52 5.02 FELDA SUNGAI PANCING 0.49 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.73 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.52 4.99 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.51 4.85 P.P.TANAMAN KAMPUNG 0.52 202 42360 AWAH PAHANG 102.5000 3.5833 31 4.99 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667Malaya 3.8333 76 0.52 4.98 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.51 4.91 PUSAT PERTANIAN BUKIT 0.50 206 42381 GOH PAHANG 103.0833 3.8333 31 4.81 207 42400 FELDA TERSANG SATU PAHANGof 101.7833 4.0000 130 0.52 5.03 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.54 5.18 LADANG FELDA KECHAU 0.55 209 42415 06 PAHANG 102.1667 4.2500 0 5.28 FELDA SUNGAI KOYAN 0.55 210 42416 SATU PAHANG 101.8667 4.2667 0 5.29 MARDI CAMERON 0.46 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 4.43 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.50 4.77 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.48 4.70 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.48 4.71 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.51 4.99 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.51 4.97 RUMAH API BKT. 0.51 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.98 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.52 5.09 219 University47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.54 5.20 KLINIK KESIHATAN 0.53 220 47203 KAHANG JOHORE 103.5333 2.2167 53 5.18 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.47 4.62 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.54 5.20 PEJ. KEJORA BANDAR 0.49 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.81 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.54 5.23 MARDI ALOR BUKIT 0.48 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.73 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.48 4.72 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.52 5.03 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.50 4.90

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229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.51 4.96 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.51 4.95 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.52 5.06 47137 KULIM MONTEL FARM 0.50 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.88 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.52 5.09 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.51 4.96 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.51 4.94 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.51 4.98 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.53 5.12 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.53 5.13 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.52 5.07 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.53 5.17 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.53 5.18 FELDA TENGGAROH 0.53 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 5.15 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.54 5.22 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.54 5.21 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.53 5.19 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.54 5.24 FELDA BUKIT 0.54 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 5.27 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.54 5.27 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 0.53 5.14 Malaya0.54 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 5.21 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.54 5.23 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.52 5.03 of

University

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Table A3 Solar radiation and clearness index prediction for month March.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.57 5.80 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.57 5.79 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.55 5.70 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.55 5.64 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.56 5.72 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.56 5.77 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.57 5.80 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.57 5.81 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.56 5.79 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.56 5.74 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.56 5.77 PUSAT PERT.BATU 0.56 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 5.79 MADA KOTA SARANG 0.56 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 5.76 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.56 5.78 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.56 5.73 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.56 5.74 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.56 5.75 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.56 5.77 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.56 5.79 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.56 5.78 MADA TELAGA BATU, 0.56 21 41615 JITRA KEDAH 100.3500 6.2333 5 5.78 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.57 5.81 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.57 5.81 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.57 5.80 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.55 5.60 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.48 4.91 KEBUN BUNGA PULAU 0.54 27 41536 PINANG P. PINANG 100.2833 5.4333 74 5.58 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.55 5.69 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.50 5.15 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.51 5.28 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.51 5.28 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.51 5.31 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.52 5.36 SEISMO. STN BUKIT 0.51 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 5.29 HOSPITAL BAHAGIA ULU 0.53 35 43419 KINTA PERAK 101.1667 4.6667 70 5.47 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.52 5.35 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.54 5.56 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.44 4.52 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.53 5.48 HOSPITAL KUALA 0.53 40 43447 KANGSAR PERAK 100.9333 4.7667 68 5.47 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.53 5.50 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.54 5.60 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.55 5.62 44 43507 POS POI PERAK 101.2167 5.1000 1524 0.38 3.95

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45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.56 5.71 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.55 5.63 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.54 5.55 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.55 5.69 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.56 5.74 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.55 5.66 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.49 5.03 PUSAT PERT. TITI 0.51 52 43353 GANTONG PERAK 100.8500 4.3667 0 5.28 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.50 5.11 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.50 5.17 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.50 5.12 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.50 5.14 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.50 5.11 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.52 5.33 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.49 5.02 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.53 5.46 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.54 5.61 CHERSONESE ESTATE 0.55 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 5.65 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.56 5.75 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.47 4.86 AMPANGAN AIR SG. 0.48 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.93 RUMAH API ONE FATHOM Malaya0.43 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.47 MARINE DEPT. PORT 0.46 67 44298 KLANG SELANGOR 101.3833 3.0167 2 4.77 NEB CONNAUGHT BRIDGE 0.47 68 44305 POWER STATION KLANG SELANGORof 101.4667 3.0500 3 4.81 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.47 4.89 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.47 4.81 HOSPITAL KUALA KUBU 0.49 71 44322 BARU SELANGOR 101.6500 3.5667 61 5.02 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.48 4.95 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.47 4.84 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.47 4.87 PUSAT PERT. TELOK 0.46 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.76 BANTING OIL PALM RES. 0.46 76 44256 STN SELANGOR 101.5000 2.8167 0 4.75 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.47 4.85 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.46 4.80 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.47 4.85 PUSAT PERTANIAN 0.48 80 University44315 BATANG KALI SELANGOR 101.6408 3.4797 46 5.00 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.47 4.89 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.47 4.88 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.50 5.12 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.49 5.01 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.47 4.81 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.48 4.93 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.47 4.82 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.47 4.86 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.47 4.89 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.48 4.92

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91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.48 4.91 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.47 4.88 UNIVERSITY MALAYA 0.47 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.81 RUMAH API TANJONG. 0.47 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.86 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.47 4.87 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.48 4.93 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.47 4.87 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.47 4.87 PUSAT PERTANIAN 0.48 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 5.01 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.49 5.02 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.49 5.07 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.49 5.06 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.49 5.08 FELDA BUKIT ROKAN 0.49 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 5.07 LADANG BATANG JELAI 0.49 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 5.02 CHEMARA RES. TANAH 0.47 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.86 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.47 4.88 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.47 4.90 LADANG AYER HITAM 0.48 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.99 111 46203 RUMAH API PULAU UNDAN MELAKA 102.3333 2.0500 53 0.47 4.85 ALAM (AKEDEMI LAUT Malaya0.48 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.94 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.48 4.99 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.48 4.96 of 0.49 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 5.04 CHEMAR RES. SERKAM 0.49 116 46211 JASIN MELAKA 102.4167 2.3228 27 5.02 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.48 4.99 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.47 4.91 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.49 5.04 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.49 5.03 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.50 5.17 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.53 5.46 RPS KUALA BETIS 0.54 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 5.54 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.50 5.18 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.55 5.67 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.53 5.41 127 University40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.54 5.58 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.55 5.65 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.56 5.73 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.54 5.53 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.57 5.83 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.56 5.72 P. TER. HAIWAN TANAH 0.57 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.86 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.57 5.86 PEJ.HAI. JAJAHAN 0.57 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 5.85 136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.57 5.86

138

137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.57 5.87 PUSAT PERTANIAN 0.57 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.87 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.57 5.87 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.57 5.87 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.57 5.87 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.51 5.27 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.53 5.49 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.49 5.07 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.53 5.45 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.49 5.00 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.54 5.59 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.49 5.09 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.50 5.12 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.50 5.11 LADANG KOKO LANDAS 0.54 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 5.61 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.55 5.63 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.55 5.69 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.55 5.63 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.55 5.68 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.56 5.77 INSTITUT PERTANIAN 0.57 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 5.84 Malaya0.49 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 5.02 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.49 5.03 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.49 5.03 PUSAT LATIHAN JHEOA Km of 0.48 161 42304 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.98 PERBADANAN KEMAJUAN 0.44 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 4.52 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.49 5.01 TALIKOM BUKIT PENINJAU 0.44 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 4.50 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.49 5.01 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.47 4.89 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.49 5.03 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.52 5.31 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.49 5.05 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.48 4.91 TAPAK SEMAIAN LENTANG, BENTONG, 0.48 171 42337 PAHANG PAHANG 101.8867 3.3900 153 4.93 172 University42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.50 5.13 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.47 4.82 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.51 5.23 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.50 5.12 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.53 5.50 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.51 5.30 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.52 5.40 PST. PERTANIAN 0.49 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 5.06 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.49 5.05 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.49 5.06 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.49 5.01

139

183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.49 5.02 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.48 4.92 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.48 4.97 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.48 4.95 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.49 5.03 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.49 5.06 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.48 5.01 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.49 5.09 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.49 5.05 PUSAT PERTANIAN JAMBU 0.49 192 42338 RIAS PAHANG 102.1500 3.4667 0 5.06 FELDA KAMPONG NEW 0.49 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 5.01 FELDA JENDERAK 0.50 194 42340 SELATAN PAHANG 102.3000 3.6833 0 5.12 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.49 5.09 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.48 4.99 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.50 5.12 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.49 5.09 FELDA SUNGAI PANCING 0.48 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.93 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.49 5.08 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.49 5.01 P.P.TANAMAN KAMPUNG 0.49 202 42360 AWAH PAHANG 102.5000 3.5833 31 5.05 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667Malaya 3.8333 76 0.50 5.11 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.50 5.11 PUSAT PERTANIAN BUKIT 0.48 206 42381 GOH PAHANG 103.0833 3.8333 31 4.99 207 42400 FELDA TERSANG SATU PAHANGof 101.7833 4.0000 130 0.51 5.21 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.51 5.26 LADANG FELDA KECHAU 0.52 209 42415 06 PAHANG 102.1667 4.2500 0 5.39 FELDA SUNGAI KOYAN 0.52 210 42416 SATU PAHANG 101.8667 4.2667 0 5.40 MARDI CAMERON 0.42 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 4.28 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.48 4.98 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.42 4.39 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.43 4.42 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.46 4.80 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.46 4.79 RUMAH API BKT. 0.46 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.72 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.48 4.95 219 University47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.48 4.98 KLINIK KESIHATAN 0.49 220 47203 KAHANG JOHORE 103.5333 2.2167 53 5.11 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.42 4.33 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.50 5.13 PEJ. KEJORA BANDAR 0.45 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.64 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.50 5.13 MARDI ALOR BUKIT 0.43 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.42 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.43 4.46 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.47 4.85 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.46 4.72

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229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.47 4.86 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.45 4.67 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.47 4.85 47137 KULIM MONTEL FARM 0.45 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.67 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.47 4.84 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.45 4.69 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.46 4.73 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.46 4.79 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.49 5.11 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.48 4.93 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.47 4.91 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.48 4.97 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.48 4.96 FELDA TENGGAROH TIMUR 0.49 242 47209 2 JOHORE 103.8667 2.0833 25 5.09 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.49 5.06 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.49 5.06 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.48 4.99 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.49 5.08 247 47220 FELDA BUKIT SERAMPANG JOHORE 102.8000 2.3333 0 0.49 5.12 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.50 5.14 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 0.50 5.15 250 47232 FELDA MEDOI JOHORE 102.8833Malaya 2.5333 46 0.49 5.11 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.49 5.11 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.49 5.09 of

University

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Table A4 Solar radiation and clearness index prediction for month April.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.56 5.68 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.56 5.69 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.55 5.53 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.54 5.45 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.55 5.55 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.56 5.65 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.56 5.67 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.57 5.71 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.56 5.69 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.56 5.60 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.56 5.64 PUSAT PERT.BATU 0.56 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 5.68 MADA KOTA SARANG 0.56 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 5.60 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.56 5.65 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.55 5.52 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.55 5.55 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.55 5.58 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.56 5.63 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.56 5.67 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.56 5.66 MADA TELAGA BATU, 0.56 21 41615 JITRA KEDAH 100.3500 6.2333 5 5.65 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.57 5.71 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.57 5.70 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.56 5.70 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.53 5.38 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.47 4.70 KEBUN BUNGA PULAU 0.53 27 41536 PINANG P. PINANG 100.2833 5.4333 74 5.34 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.55 5.51 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.51 5.06 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.52 5.20 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.52 5.20 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.52 5.20 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.52 5.23 SEISMO. STN BUKIT 0.51 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 5.12 HOSPITAL BAHAGIA ULU 0.53 35 43419 KINTA PERAK 101.1667 4.6667 70 5.34 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.51 5.16 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.54 5.42 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.44 4.40 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.53 5.30 HOSPITAL KUALA 0.53 40 43447 KANGSAR PERAK 100.9333 4.7667 68 5.30 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.53 5.31 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.54 5.43 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.55 5.48 44 43507 POS POI PERAK 101.2167 5.1000 1524 0.40 4.02

142

45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.55 5.57 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.55 5.49 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.53 5.35 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.55 5.55 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.56 5.63 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.55 5.52 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.50 4.96 PUSAT PERT. TITI 0.51 52 43353 GANTONG PERAK 100.8500 4.3667 0 5.15 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.50 5.02 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.51 5.11 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.50 5.02 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.50 5.05 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.50 4.99 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.52 5.19 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.49 4.92 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.53 5.29 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.54 5.47 CHERSONESE ESTATE 0.55 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 5.52 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.56 5.66 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.49 4.91 AMPANGAN AIR SG. 0.50 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.98 RUMAH API ONE FATHOM Malaya0.45 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.44 MARINE DEPT. PORT 0.48 67 44298 KLANG SELANGOR 101.3833 3.0167 2 4.77 NEB CONNAUGHT BRIDGE 0.48 68 44305 POWER STATION KLANG SELANGORof 101.4667 3.0500 3 4.82 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.49 4.93 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.49 4.86 HOSPITAL KUALA KUBU 0.50 71 44322 BARU SELANGOR 101.6500 3.5667 61 5.03 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.50 4.99 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.49 4.89 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.49 4.91 PUSAT PERT. TELOK 0.48 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.79 BANTING OIL PALM RES. 0.48 76 44256 STN SELANGOR 101.5000 2.8167 0 4.78 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.49 4.89 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.48 4.81 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.49 4.88 PUSAT PERTANIAN 0.50 80 University44315 BATANG KALI SELANGOR 101.6408 3.4797 46 5.00 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.48 4.84 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.48 4.84 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.51 5.14 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.50 4.99 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.48 4.81 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.49 4.92 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.48 4.82 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.49 4.84 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.49 4.87 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.49 4.90

143

91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.49 4.94 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.49 4.92 UNIVERSITY MALAYA 0.49 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.85 RUMAH API TANJONG. 0.49 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.92 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.50 4.94 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.50 5.02 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.50 4.94 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.50 4.94 PUSAT PERTANIAN 0.51 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 5.08 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.51 5.09 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.52 5.15 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.52 5.14 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.52 5.17 FELDA BUKIT ROKAN 0.52 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 5.14 LADANG BATANG JELAI 0.51 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 5.11 CHEMARA RES. TANAH 0.49 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.92 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.50 4.94 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.50 4.96 LADANG AYER HITAM 0.51 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 5.08 111 46203 RUMAH API PULAU UNDAN MELAKA 102.3333 2.0500 53 0.49 4.90 ALAM (AKEDEMI LAUT Malaya0.50 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.99 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.51 5.03 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.50 5.01 of 0.51 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 5.08 CHEMAR RES. SERKAM 0.51 116 46211 JASIN MELAKA 102.4167 2.3228 27 5.07 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.51 5.05 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.50 4.97 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.51 5.09 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.51 5.08 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.50 4.98 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.53 5.34 RPS KUALA BETIS 0.54 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 5.42 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.50 5.00 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.56 5.58 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.52 5.26 127 University40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.54 5.44 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.55 5.54 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.56 5.63 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.54 5.41 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.57 5.77 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.56 5.63 P. TER. HAIWAN TANAH 0.58 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.81 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.58 5.82 PEJ.HAI. JAJAHAN 0.58 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 5.81 136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.58 5.81

144

137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.58 5.84 PUSAT PERTANIAN 0.58 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.84 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.58 5.84 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.58 5.85 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.58 5.84 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.51 5.15 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.54 5.40 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.50 5.00 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.53 5.32 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.50 4.98 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.55 5.49 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.50 5.04 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.51 5.07 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.50 5.03 LADANG KOKO LANDAS 0.55 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 5.50 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.55 5.53 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.56 5.60 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.55 5.52 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.56 5.59 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.57 5.69 INSTITUT PERTANIAN 0.58 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 5.80 Malaya0.52 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 5.14 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.51 5.12 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.52 5.14 PUSAT LATIHAN JHEOA Km of 0.50 161 42304 56 (Bt. 34) PAHANG 102.9167 3.6000 0 5.03 PERBADANAN KEMAJUAN 0.42 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 4.15 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.50 5.04 TALIKOM BUKIT PENINJAU 0.41 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 4.13 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.51 5.09 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.50 4.98 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.51 5.12 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.52 5.25 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.51 5.09 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.50 4.95 TAPAK SEMAIAN LENTANG, BENTONG, 0.50 171 42337 PAHANG PAHANG 101.8867 3.3900 153 4.97 172 University42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.52 5.16 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.49 4.89 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.52 5.20 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.50 5.00 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.54 5.40 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.52 5.17 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.53 5.31 PST. PERTANIAN 0.52 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 5.18 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.52 5.14 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.51 5.11 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.51 5.09

145

183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.51 5.09 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.50 5.00 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.51 5.07 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.51 5.05 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.51 5.07 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.51 5.08 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.51 5.06 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.51 5.09 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.51 5.09 PUSAT PERTANIAN JAMBU 0.51 192 42338 RIAS PAHANG 102.1500 3.4667 0 5.11 FELDA KAMPONG NEW 0.51 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 5.05 FELDA JENDERAK 0.51 194 42340 SELATAN PAHANG 102.3000 3.6833 0 5.15 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.51 5.13 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.51 5.09 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.51 5.13 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.51 5.12 FELDA SUNGAI PANCING 0.49 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.94 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.51 5.11 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.50 5.01 P.P.TANAMAN KAMPUNG 0.51 202 42360 AWAH PAHANG 102.5000 3.5833 31 5.10 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667Malaya 3.8333 76 0.51 5.12 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.51 5.10 PUSAT PERTANIAN BUKIT 0.50 206 42381 GOH PAHANG 103.0833 3.8333 31 5.00 207 42400 FELDA TERSANG SATU PAHANGof 101.7833 4.0000 130 0.52 5.17 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.52 5.25 LADANG FELDA KECHAU 0.53 209 42415 06 PAHANG 102.1667 4.2500 0 5.34 FELDA SUNGAI KOYAN 0.53 210 42416 SATU PAHANG 101.8667 4.2667 0 5.34 MARDI CAMERON 0.41 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 4.16 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.49 4.94 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.45 4.43 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.45 4.45 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.49 4.82 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.48 4.80 RUMAH API BKT. 0.48 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.73 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.50 4.95 219 University47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.50 5.01 KLINIK KESIHATAN 0.52 220 47203 KAHANG JOHORE 103.5333 2.2167 53 5.15 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.44 4.36 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.52 5.18 PEJ. KEJORA BANDAR 0.47 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.65 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.52 5.18 MARDI ALOR BUKIT 0.45 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.45 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.45 4.48 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.49 4.85 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.48 4.74

146

229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.49 4.87 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.47 4.69 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.49 4.86 47137 KULIM MONTEL FARM 0.47 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.68 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.49 4.86 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.47 4.70 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.48 4.73 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.48 4.79 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.52 5.15 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.50 4.94 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.49 4.91 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.50 4.98 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.50 4.99 FELDA TENGGAROH 0.52 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 5.13 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.51 5.08 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.51 5.09 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.50 5.01 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.51 5.12 FELDA BUKIT 0.52 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 5.17 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.52 5.19 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 0.53 5.24 Malaya0.52 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 5.18 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.52 5.19 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.52 5.20 of

University

147

Table A5 Solar radiation and clearness index prediction for month May.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.51 5.31 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.51 5.33 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.50 5.18 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.49 5.11 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.50 5.16 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.51 5.32 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.51 5.29 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.52 5.38 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.51 5.33 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.51 5.29 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.51 5.28 PUSAT PERT.BATU 0.51 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 5.31 MADA KOTA SARANG 0.50 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 5.17 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.50 5.25 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.47 4.92 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.48 4.97 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.49 5.09 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.50 5.22 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.51 5.30 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.50 5.22 MADA TELAGA BATU, 0.50 21 41615 JITRA KEDAH 100.3500 6.2333 5 5.23 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.52 5.38 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.51 5.37 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.51 5.34 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.48 4.94 PUSAT KES. BKT. 0.38 26 41533 BENDERA P. PINANG 100.2667 5.4167 732 3.98 KEBUN BUNGA PULAU 0.47 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.87 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.49 5.13 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.49 4.99 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.50 5.08 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.50 5.08 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.50 5.09 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.50 5.11 SEISMO. STN BUKIT 0.48 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 4.94 HOSPITAL BAHAGIA ULU 0.50 35 43419 KINTA PERAK 101.1667 4.6667 70 5.16 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.47 4.86 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.50 5.19 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.39 3.99 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.50 5.12 HOSPITAL KUALA 0.50 40 43447 KANGSAR PERAK 100.9333 4.7667 68 5.12 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.49 5.08 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.50 5.16 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.50 5.19

148

44 43507 POS POI PERAK 101.2167 5.1000 1524 0.39 4.04 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.51 5.27 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.50 5.14 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.49 5.08 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.50 5.19 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.51 5.27 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.48 5.01 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.48 4.89 PUSAT PERT. TITI 0.49 52 43353 GANTONG PERAK 100.8500 4.3667 0 5.07 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.48 4.96 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.49 5.00 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.48 4.96 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.49 4.98 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.48 4.95 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.50 5.09 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.48 4.88 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.50 5.11 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.51 5.26 CHERSONESE ESTATE 0.51 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 5.28 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.52 5.41 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.47 4.76 AMPANGAN AIR SG. Malaya0.48 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.83 RUMAH API ONE FATHOM 0.43 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.35 MARINE DEPT. PORT 0.46 67 44298 KLANG SELANGORof 101.3833 3.0167 2 4.64 NEB CONNAUGHT BRIDGE 0.46 68 44305 POWER STATION KLANG SELANGOR 101.4667 3.0500 3 4.69 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.47 4.78 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.46 4.69 HOSPITAL KUALA KUBU 0.48 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.90 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.48 4.86 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.47 4.74 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.47 4.77 PUSAT PERT. TELOK 0.46 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.65 BANTING OIL PALM RES. 0.46 76 44256 STN SELANGOR 101.5000 2.8167 0 4.64 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.47 4.74 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.46 4.67 79 University44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.47 4.74 PUSAT PERTANIAN 0.48 80 44315 BATANG KALI SELANGOR 101.6408 3.4797 46 4.88 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.47 4.75 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.46 4.74 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.49 5.00 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.48 4.88 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.46 4.68 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.47 4.81 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.46 4.69 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.46 4.73 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.47 4.77

149

90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.47 4.79 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.47 4.81 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.47 4.78 UNIVERSITY MALAYA 0.46 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.70 RUMAH API TANJONG. 0.47 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.78 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.47 4.78 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.48 4.85 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.47 4.78 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.47 4.78 PUSAT PERTANIAN 0.49 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.91 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.49 4.92 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.49 5.00 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.49 4.98 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.50 5.03 FELDA BUKIT ROKAN 0.49 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 5.00 LADANG BATANG JELAI 0.49 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.95 CHEMARA RES. TANAH 0.47 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.77 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.47 4.79 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.47 4.81 LADANG AYER HITAM 0.49 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.93 RUMAH API PULAU Malaya0.47 111 46203 UNDAN MELAKA 102.3333 2.0500 53 4.75 ALAM (AKEDEMI LAUT 0.48 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.84 113 46206 MERLIMAU ESTATE MELAKAof 102.4333 2.1833 0 0.48 4.87 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.48 4.85 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.49 4.92 CHEMAR RES. SERKAM 0.49 116 46211 JASIN MELAKA 102.4167 2.3228 27 4.91 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.48 4.88 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.48 4.82 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.49 4.92 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.49 4.92 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.46 4.69 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.49 5.09 RPS KUALA BETIS 0.50 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 5.18 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.46 4.71 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.51 5.30 University 0.47 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 4.90 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.50 5.13 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.51 5.24 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.52 5.38 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.48 5.00 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.53 5.54 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.52 5.36 P. TER. HAIWAN TANAH 0.54 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.65 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.54 5.65 PEJ.HAI. JAJAHAN 0.54 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 5.64

150

136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.54 5.64 137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.55 5.70 PUSAT PERTANIAN 0.55 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.72 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.55 5.70 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.55 5.72 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.55 5.72 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.48 4.91 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.50 5.12 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.47 4.80 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.49 5.05 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.47 4.79 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.51 5.21 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.47 4.84 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.47 4.86 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.47 4.83 LADANG KOKO LANDAS 0.51 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 5.23 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.51 5.26 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.52 5.34 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.51 5.25 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.52 5.33 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.53 5.46 INSTITUT PERTANIAN Malaya0.54 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 5.62 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 0.50 5.03 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.49 5.00 of 0.49 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 5.02 PUSAT LATIHAN JHEOA 0.48 161 42304 Km 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.89 PERBADANAN KEMAJUAN 0.41 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 4.16 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.48 4.90 TALIKOM BUKIT PENINJAU 0.41 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 4.15 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.49 4.96 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.48 4.86 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.49 5.00 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.49 5.07 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.49 4.96 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.47 4.80 TAPAK SEMAIAN LENTANG, BENTONG, 0.47 171 University42337 PAHANG PAHANG 101.8867 3.3900 153 4.82 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.49 5.01 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.47 4.77 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.49 5.02 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.47 4.81 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.51 5.22 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.48 4.95 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.50 5.11 PST. PERTANIAN 0.50 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 5.07 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.49 5.01 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.49 4.97

151

182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.49 4.95 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.49 4.95 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.48 4.87 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.49 4.96 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.49 4.94 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.48 4.93 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.48 4.94 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.48 4.91 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.48 4.93 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.48 4.94 PUSAT PERTANIAN JAMBU 0.49 192 42338 RIAS PAHANG 102.1500 3.4667 0 4.97 FELDA KAMPONG NEW 0.48 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.90 FELDA JENDERAK 0.49 194 42340 SELATAN PAHANG 102.3000 3.6833 0 5.00 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.49 4.97 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.49 4.97 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.49 5.00 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.49 4.97 FELDA SUNGAI PANCING 0.47 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.78 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.48 4.95 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.47 4.83 P.P.TANAMAN KAMPUNG 0.48 202 42360 AWAH PAHANG 102.5000Malaya 3.5833 31 4.95 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.48 4.95 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.48 4.91 PUSAT PERTANIAN BUKIT 0.47 206 42381 GOH PAHANGof 103.0833 3.8333 31 4.83 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.49 5.01 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.49 5.06 LADANG FELDA KECHAU 0.50 209 42415 06 PAHANG 102.1667 4.2500 0 5.15 FELDA SUNGAI KOYAN 0.50 210 42416 SATU PAHANG 101.8667 4.2667 0 5.16 MARDI CAMERON 0.40 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 4.11 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.46 4.76 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.42 4.26 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.42 4.25 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.46 4.59 JKR BANDAR KOTA 0.46 216 47121 TINGGI JOHORE 103.9000 1.7333 33 4.57 RUMAH API BKT. 0.45 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.56 University 0.47 218 47159 SINDORA JOHORE 103.4711 1.9636 81 4.74 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.48 4.84 KLINIK KESIHATAN 0.49 220 47203 KAHANG JOHORE 103.5333 2.2167 53 4.98 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.42 4.19 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.50 5.02 PEJ. KEJORA BANDAR 0.44 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.42 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.50 5.02 MARDI ALOR BUKIT 0.43 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.27 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.43 4.27 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.46 4.63

152

228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.45 4.50 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.46 4.65 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.45 4.50 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.46 4.64 47137 KULIM MONTEL FARM 0.44 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.45 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.47 4.69 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.45 4.49 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.45 4.51 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.45 4.56 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.49 4.98 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.47 4.73 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.47 4.70 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.47 4.78 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.48 4.81 FELDA TENGGAROH 0.49 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 4.94 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.48 4.89 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.48 4.89 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.48 4.82 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.49 4.94 FELDA BUKIT 0.50 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 5.00 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.50 5.03 Malaya0.50 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 5.10 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.50 5.03 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.50 5.04 of 0.50 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 5.08

University

153

Table A6 Solar radiation and clearness index prediction for month June.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.51 5.03 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.50 4.99 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.52 5.10 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.51 5.02 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.52 5.07 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.54 5.25 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.52 5.09 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.53 5.21 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.53 5.19 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.53 5.21 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.53 5.19 PUSAT PERT.BATU 0.53 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 5.19 MADA KOTA SARANG 0.51 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 5.04 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.52 5.12 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.48 4.70 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.48 4.71 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.50 4.93 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.52 5.08 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.52 5.12 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.51 5.04 MADA TELAGA BATU, 0.51 21 41615 JITRA KEDAH 100.3500 6.2333 5 5.03 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.52 5.15 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.52 5.11 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.51 5.08 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.50 4.87 PUSAT KES. BKT. 0.40 26 41533 BENDERA P. PINANG 100.2667 5.4167 732 3.94 KEBUN BUNGA PULAU 0.49 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.80 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.52 5.05 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.49 4.74 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.51 4.90 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.51 4.90 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.51 4.90 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.51 4.94 SEISMO. STN BUKIT 0.49 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 4.76 HOSPITAL BAHAGIA ULU 0.52 35 43419 KINTA PERAK 101.1667 4.6667 70 5.03 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.49 4.74 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.52 5.10 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.41 3.98 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.51 4.98 HOSPITAL KUALA 0.51 40 43447 KANGSAR PERAK 100.9333 4.7667 68 4.98 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.51 4.95 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.52 5.06 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.53 5.12

154

44 43507 POS POI PERAK 101.2167 5.1000 1524 0.40 3.93 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.53 5.21 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.52 5.08 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.51 4.97 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.52 5.13 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.53 5.22 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.50 4.94 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.48 4.58 PUSAT PERT. TITI 0.50 52 43353 GANTONG PERAK 100.8500 4.3667 0 4.85 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.49 4.68 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.50 4.78 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.49 4.68 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.49 4.73 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.48 4.67 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.51 4.90 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.47 4.56 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.51 4.97 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.53 5.17 CHERSONESE ESTATE 0.54 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 5.21 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.55 5.39 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.46 4.42 AMPANGAN AIR SG. Malaya0.47 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.51 RUMAH API ONE FATHOM 0.42 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.04 MARINE DEPT. PORT 0.45 67 44298 KLANG SELANGORof 101.3833 3.0167 2 4.30 NEB CONNAUGHT BRIDGE 0.46 68 44305 POWER STATION KLANG SELANGOR 101.4667 3.0500 3 4.35 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.47 4.49 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.46 4.44 HOSPITAL KUALA KUBU 0.48 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.65 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.48 4.56 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.46 4.39 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.47 4.44 PUSAT PERT. TELOK 0.45 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.32 BANTING OIL PALM RES. 0.45 76 44256 STN SELANGOR 101.5000 2.8167 0 4.31 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.46 4.42 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.45 4.33 79 University44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.46 4.44 PUSAT PERTANIAN 0.48 80 44315 BATANG KALI SELANGOR 101.6408 3.4797 46 4.61 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.46 4.41 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.46 4.41 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.50 4.80 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.48 4.62 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.46 4.35 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.47 4.50 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.46 4.36 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.46 4.40 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.46 4.44

155

90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.47 4.47 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.47 4.49 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.47 4.47 UNIVERSITY MALAYA 0.46 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.40 RUMAH API TANJONG. 0.47 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.42 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.47 4.45 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.48 4.53 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.47 4.49 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.47 4.43 PUSAT PERTANIAN 0.48 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.55 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.48 4.56 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.49 4.65 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.49 4.64 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.49 4.70 FELDA BUKIT ROKAN 0.49 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 4.64 LADANG BATANG JELAI 0.49 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.62 CHEMARA RES. TANAH 0.47 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.42 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.47 4.44 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.47 4.46 LADANG AYER HITAM 0.49 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.64 RUMAH API PULAU Malaya0.46 111 46203 UNDAN MELAKA 102.3333 2.0500 53 4.35 ALAM (AKEDEMI LAUT 0.47 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.46 113 46206 MERLIMAU ESTATE MELAKAof 102.4333 2.1833 0 0.47 4.43 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.47 4.43 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.47 4.49 CHEMAR RES. SERKAM 0.48 116 46211 JASIN MELAKA 102.4167 2.3228 27 4.50 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.47 4.49 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.47 4.44 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.48 4.52 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.48 4.51 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.47 4.59 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.52 5.01 RPS KUALA BETIS 0.53 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 5.11 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.47 4.59 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.54 5.28 University 0.49 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 4.80 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.52 5.06 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.53 5.20 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.55 5.34 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.51 4.93 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.56 5.51 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.55 5.34 P. TER. HAIWAN TANAH 0.57 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.61 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.57 5.61 PEJ.HAI. JAJAHAN 0.57 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 5.60

156

136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.57 5.61 137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.58 5.65 PUSAT PERTANIAN 0.58 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.67 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.57 5.65 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.58 5.67 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.58 5.66 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.49 4.73 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.52 5.05 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.48 4.60 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.50 4.87 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.48 4.61 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.52 5.09 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.48 4.67 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.48 4.68 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.48 4.64 LADANG KOKO LANDAS 0.53 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 5.12 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.53 5.15 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.54 5.26 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.53 5.13 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.54 5.24 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.55 5.38 INSTITUT PERTANIAN Malaya0.57 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 5.57 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 0.51 4.82 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.49 4.72 of 0.50 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 4.77 PUSAT LATIHAN JHEOA 0.49 161 42304 Km 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.69 PERBADANAN KEMAJUAN 0.42 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 4.07 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.49 4.68 TALIKOM BUKIT PENINJAU 0.42 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 4.07 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.49 4.72 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.49 4.64 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.50 4.72 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.51 4.95 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.49 4.71 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.47 4.54 TAPAK SEMAIAN LENTANG, BENTONG, 0.48 171 University42337 PAHANG PAHANG 101.8867 3.3900 153 4.58 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.50 4.83 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.48 4.56 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.51 4.90 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.48 4.67 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.53 5.13 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.50 4.83 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.52 5.01 PST. PERTANIAN 0.51 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 4.85 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.50 4.72 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.50 4.75

157

182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.49 4.72 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.49 4.70 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.49 4.65 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.49 4.72 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.49 4.72 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.49 4.71 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.49 4.74 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.49 4.71 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.49 4.75 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.49 4.75 PUSAT PERTANIAN JAMBU 0.49 192 42338 RIAS PAHANG 102.1500 3.4667 0 4.74 FELDA KAMPONG NEW 0.49 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.71 FELDA JENDERAK 0.50 194 42340 SELATAN PAHANG 102.3000 3.6833 0 4.81 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.50 4.79 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.50 4.73 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.50 4.79 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.50 4.79 FELDA SUNGAI PANCING 0.48 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.61 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.50 4.78 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.48 4.65 P.P.TANAMAN KAMPUNG 0.50 202 42360 AWAH PAHANG 102.5000Malaya 3.5833 31 4.75 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.50 4.80 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.50 4.77 PUSAT PERTANIAN BUKIT 0.48 206 42381 GOH PAHANGof 103.0833 3.8333 31 4.65 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.50 4.86 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.51 4.94 LADANG FELDA KECHAU 0.52 209 42415 06 PAHANG 102.1667 4.2500 0 5.05 FELDA SUNGAI KOYAN 0.52 210 42416 SATU PAHANG 101.8667 4.2667 0 5.06 MARDI CAMERON 0.42 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 4.06 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.47 4.56 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.42 3.97 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.42 3.98 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.45 4.26 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.45 4.25 RUMAH API BKT. 0.44 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.17 218 University47159 SINDORA JOHORE 103.4711 1.9636 81 0.46 4.37 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.47 4.39 KLINIK KESIHATAN 0.49 220 47203 KAHANG JOHORE 103.5333 2.2167 53 4.64 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.42 3.93 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.49 4.68 PEJ. KEJORA BANDAR 0.44 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.12 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.49 4.64 MARDI ALOR BUKIT 0.42 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 3.98 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.43 4.00 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.45 4.28

158

228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.44 4.18 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.46 4.32 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.44 4.13 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.45 4.27 47137 KULIM MONTEL FARM 0.44 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.15 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.45 4.25 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.44 4.13 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.44 4.18 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.45 4.23 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.49 4.67 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.46 4.33 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.46 4.33 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.46 4.36 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.46 4.38 FELDA TENGGAROH 0.49 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 4.61 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.47 4.47 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.48 4.50 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.47 4.39 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.48 4.53 FELDA BUKIT 0.48 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 4.59 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.49 4.63 Malaya0.51 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 4.84 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.49 4.68 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.49 4.69 of 0.51 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 4.86

University

159

Table A7 Solar radiation and clearness index prediction for month July.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.46 4.88 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.47 4.91 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.45 4.74 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.45 4.72 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.45 4.74 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.46 4.81 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.46 4.84 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.46 4.85 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.45 4.76 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.46 4.84 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.46 4.78 12 41549 PUSAT PERT.BATU SEKETOL KEDAH 100.8000 5.9667 71 0.45 4.77 MADA KOTA SARANG SEMUT, 0.45 13 41551 PENDANG KEDAH 100.3833 5.9667 5 4.75 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.45 4.77 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.44 4.58 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.43 4.57 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.45 4.71 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.45 4.77 19 41611 PERTANIAN GAJAH MATI KEDAH Malaya100.5333 6.1667 15 0.46 4.83 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.45 4.72 21 41615 MADA TELAGA BATU, JITRA KEDAH 100.3500 6.2333 5 0.46 4.81 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.47 4.93 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.47 4.93 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.46 4.86 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.44 4.62 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.36 3.79 KEBUN BUNGA PULAU 0.44 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.55 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.45 4.74 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.47 4.86 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.48 4.93 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.48 4.93 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.48 4.93 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.48 4.94 34 43418 SEISMO. STN BUKIT KLEDANG PERAK 101.0167 4.5833 240 0.45 4.71 HOSPITAL BAHAGIA ULU 0.48 35 University43419 KINTA PERAK 101.1667 4.6667 70 4.93 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.44 4.52 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.47 4.89 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.37 3.81 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.47 4.87 40 43447 HOSPITAL KUALA KANGSAR PERAK 100.9333 4.7667 68 0.47 4.87 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.46 4.81 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.46 4.79 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.46 4.80 44 43507 POS POI PERAK 101.2167 5.1000 1524 0.38 3.91 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.46 4.81

160

46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.45 4.66 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.46 4.78 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.45 4.65 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.45 4.68 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.42 4.40 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.46 4.73 52 43353 PUSAT PERT. TITI GANTONG PERAK 100.8500 4.3667 0 0.48 4.92 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.47 4.82 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.47 4.85 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.47 4.82 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.47 4.85 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.47 4.82 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.48 4.93 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.46 4.73 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.47 4.87 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.48 4.97 CHERSONESE ESTATE KUALA 0.47 62 43500 KURAU PERAK 101.4333 5.0000 94 4.92 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.48 5.02 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.45 4.56 AMPANGAN AIR SG. 0.45 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.62 RUMAH API ONE FATHOM 0.41 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.23 Malaya0.43 67 44298 MARINE DEPT. PORT KLANG SELANGOR 101.3833 3.0167 2 4.43 NEB CONNAUGHT BRIDGE 0.44 68 44305 POWER STATION KLANG SELANGOR 101.4667 3.0500 3 4.48 69 44316 JHEOA ULU GOMBAK SELANGORof 101.7333 3.2833 109 0.45 4.58 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.44 4.48 HOSPITAL KUALA KUBU 0.46 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.73 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.46 4.66 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.45 4.55 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.45 4.55 75 44255 PUSAT PERT. TELOK DATOK SELANGOR 101.5158 2.8167 0 0.44 4.47 76 44256 BANTING OIL PALM RES. STN SELANGOR 101.5000 2.8167 0 0.44 4.46 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.44 4.53 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.44 4.47 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.44 4.53 PUSAT PERTANIAN BATANG 0.46 80 44315 KALI SELANGOR 101.6408 3.4797 46 4.70 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.44 4.56 82 University44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.44 4.55 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.47 4.86 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.46 4.71 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.44 4.48 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.45 4.61 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.44 4.48 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.44 4.53 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.45 4.56 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.45 4.60 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.45 4.60 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.45 4.57

161

UNIVERSITY MALAYA KUALA 0.44 93 48307 LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.48 95 45224 RUMAH API TANJONG. TUAN N. SEMBILAN 101.8500 2.4000 0 0.46 4.63 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.45 4.57 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.45 4.63 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.45 4.56 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.45 4.59 PUSAT PERTANIAN 0.46 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.69 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.46 4.70 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.47 4.77 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.47 4.75 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.47 4.82 105 45242 FELDA BUKIT ROKAN UTARA N. SEMBILAN 102.4167 2.6667 0 0.47 4.77 LADANG BATANG JELAI 0.46 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.73 CHEMARA RES. TANAH 0.45 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.58 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.45 4.58 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.45 4.60 LADANG AYER HITAM 0.46 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.73 111 46203 RUMAH API PULAU UNDAN MELAKA 102.3333 2.0500 53 0.45 4.60 ALAM (AKEDEMI LAUT 0.46 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.67 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.46 4.67 114 46207 DEVON ESTATE MELAKA Malaya102.4167 2.2000 42 0.46 4.65 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.46 4.70 116 46211 CHEMAR RES. SERKAM JASIN MELAKA 102.4167 2.3228 27 0.46 4.69 117 46212 FELDA KEMENDOR MELAKAof 102.4000 2.3500 69 0.46 4.66 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.46 4.65 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.46 4.70 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.46 4.71 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.42 4.35 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.46 4.81 123 40432 RPS KUALA BETIS (LAMBOK) KELANTAN 101.7500 4.7000 153 0.47 4.91 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.42 4.34 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.48 4.96 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.43 4.43 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.45 4.71 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.46 4.82 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.48 4.99 130 University40516 POS GOB KELANTAN 101.6333 5.2833 457 0.42 4.41 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.47 4.97 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.48 4.99 P. TER. HAIWAN TANAH 0.49 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.14 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.49 5.14 135 40586 PEJ.HAI. JAJAHAN MACHANG KELANTAN 102.2000 5.7667 31 0.49 5.12 136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.49 5.11 137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.50 5.20 138 40660 PUSAT PERTANIAN BACHOK KELANTAN 102.4000 6.0500 3 0.50 5.22 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.49 5.17

162

140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.50 5.22 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.50 5.21 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.45 4.69 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.47 4.86 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.45 4.64 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.46 4.77 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.45 4.67 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.47 4.90 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.45 4.70 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.46 4.70 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.45 4.66 LADANG KOKO LANDAS 0.47 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 4.91 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.47 4.92 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.48 4.98 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.47 4.91 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.48 4.98 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.48 5.04 157 49539 INSTITUT PERTANIAN BESUT TERENGGANU 102.6167 5.6500 10 0.49 5.17 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 0.48 4.90 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.47 4.83 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.48 4.88 PUSAT LATIHAN JHEOA Km 56 0.47 161 42304 (Bt. 34) PAHANG Malaya102.9167 3.6000 0 4.78 PERBADANAN KEMAJUAN 0.38 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 3.95 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.46 4.73 TALIKOM BUKIT PENINJAU of 0.38 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 3.94 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.47 4.82 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.47 4.76 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.47 4.82 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.48 4.91 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.47 4.79 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.45 4.60 TAPAK SEMAIAN LENTANG, 0.45 171 42337 BENTONG, PAHANG PAHANG 101.8867 3.3900 153 4.63 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.47 4.88 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.46 4.68 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.47 4.88 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.45 4.60 176 University42420 POS TERISU PAHANG 101.4667 4.5333 0 0.49 5.03 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.46 4.71 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.48 4.93 PST. PERTANIAN 0.48 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 4.92 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.47 4.83 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.47 4.83 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.47 4.82 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.47 4.80 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.46 4.76 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.47 4.82 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.47 4.83

163

187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.46 4.77 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.47 4.78 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.47 4.79 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.46 4.78 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.47 4.82 PUSAT PERTANIAN JAMBU 0.47 192 42338 RIAS PAHANG 102.1500 3.4667 0 4.82 FELDA KAMPONG NEW 0.47 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.80 194 42340 FELDA JENDERAK SELATAN PAHANG 102.3000 3.6833 0 0.47 4.87 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.47 4.84 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.47 4.83 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.47 4.85 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.47 4.85 FELDA SUNGAI PANCING 0.45 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.68 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.47 4.83 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.46 4.71 P.P.TANAMAN KAMPUNG 0.47 202 42360 AWAH PAHANG 102.5000 3.5833 31 4.82 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.47 4.83 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.46 4.78 PUSAT PERTANIAN BUKIT 0.46 206 42381 GOH PAHANG 103.0833 3.8333 31 4.72 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.47 4.86 208 42403 FELDA PADANG PIOL PAHANG Malaya102.3833 4.0333 0 0.48 4.93 209 42415 LADANG FELDA KECHAU 06 PAHANG 102.1667 4.2500 0 0.48 4.99 210 42416 FELDA SUNGAI KOYAN SATU PAHANG 101.8667 4.2667 0 0.48 5.01 MARDI CAMERON 0.38 211 42421 HIGHLANDS PAHANGof 101.3872 4.4681 1448 3.93 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.45 4.63 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.41 4.10 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.40 4.05 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.42 4.28 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.42 4.27 217 47139 RUMAH API BKT. SEGENTING JOHORE 102.8833 1.8000 77 0.43 4.38 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.44 4.44 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.46 4.62 220 47203 KLINIK KESIHATAN KAHANG JOHORE 103.5333 2.2167 53 0.46 4.69 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.40 4.03 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.47 4.74 PEJ. KEJORA BANDAR 0.41 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.13 University 0.47 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 4.76 225 47116 MARDI ALOR BUKIT PONTIAN JOHORE 103.4500 1.5000 3 0.41 4.11 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.40 4.05 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.43 4.33 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.42 4.21 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.43 4.32 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.43 4.31 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.43 4.35 47137 KULIM MONTEL FARM 0.41 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.17 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.45 4.50

164

234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.42 4.25 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.42 4.24 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.42 4.28 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.46 4.69 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.44 4.45 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.44 4.41 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.44 4.50 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.45 4.58 242 47209 FELDA TENGGAROH TIMUR 2 JOHORE 103.8667 2.0833 25 0.46 4.63 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.45 4.59 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.45 4.60 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.45 4.58 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.46 4.68 247 47220 FELDA BUKIT SERAMPANG JOHORE 102.8000 2.3333 0 0.47 4.74 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.47 4.77 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 0.48 4.89 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.47 4.79 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.47 4.82 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.48 4.92

Malaya

University

165

Table A8 Solar radiation and clearness index prediction for month August.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.46 4.87 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.47 4.93 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.44 4.65 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.44 4.64 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.45 4.67 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.45 4.69 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.46 4.81 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.46 4.79 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.45 4.68 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.45 4.73 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.45 4.71 PUSAT PERT.BATU 0.45 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 4.69 MADA KOTA SARANG 0.45 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 4.71 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.45 4.71 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.44 4.65 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.44 4.63 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.45 4.71 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.45 4.74 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.46 4.79 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.45 4.69 MADA TELAGA BATU, 0.46 21 41615 JITRA KEDAH 100.3500 6.2333 5 4.79 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.47 4.91 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.47 4.92 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.46 4.84 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.44 4.58 PUSAT KES. BKT. 0.37 26 41533 BENDERA P. PINANG 100.2667 5.4167 732 3.86 KEBUN BUNGA PULAU 0.43 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.52 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.45 4.66 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.46 4.73 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.46 4.82 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.46 4.82 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.46 4.82 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.46 4.82 SEISMO. STN BUKIT 0.44 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 4.57 HOSPITAL BAHAGIA ULU 0.46 35 43419 KINTA PERAK 101.1667 4.6667 70 4.81 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.42 4.39 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.46 4.76 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.37 3.83 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.46 4.75 HOSPITAL KUALA 0.46 40 43447 KANGSAR PERAK 100.9333 4.7667 68 4.74 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.45 4.70 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.45 4.66 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.45 4.65

166

44 43507 POS POI PERAK 101.2167 5.1000 1524 0.37 3.87 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.45 4.66 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.43 4.52 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.45 4.68 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.43 4.53 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.43 4.53 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.41 4.32 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.44 4.61 PUSAT PERT. TITI 0.46 52 43353 GANTONG PERAK 100.8500 4.3667 0 4.79 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.45 4.69 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.46 4.74 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.45 4.69 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.46 4.72 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.45 4.69 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.46 4.81 MARDI HILIR PERAK 0.44 59 43426 (LKM) PERAK 100.8667 3.8833 9 4.60 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.46 4.74 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.46 4.84 CHERSONESE ESTATE 0.46 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 4.78 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.47 4.87 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.43 4.45 AMPANGAN AIR SG. Malaya0.43 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.48 RUMAH API ONE FATHOM 0.40 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.16 MARINE DEPT. PORT 0.42 67 44298 KLANG SELANGORof 101.3833 3.0167 2 4.31 NEB CONNAUGHT BRIDGE 0.42 68 44305 POWER STATION KLANG SELANGOR 101.4667 3.0500 3 4.35 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.43 4.42 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.42 4.32 HOSPITAL KUALA KUBU 0.44 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.60 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.44 4.52 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.43 4.45 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.43 4.41 PUSAT PERT. TELOK 0.42 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.36 BANTING OIL PALM RES. 0.42 76 44256 STN SELANGOR 101.5000 2.8167 0 4.35 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.43 4.38 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.42 4.34 79 University44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.42 4.38 PUSAT PERTANIAN 0.44 80 44315 BATANG KALI SELANGOR 101.6408 3.4797 46 4.55 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.43 4.42 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.43 4.41 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.46 4.75 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.44 4.56 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.42 4.34 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.43 4.47 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.42 4.35 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.42 4.39 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.43 4.42

167

90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.43 4.46 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.43 4.45 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.43 4.41 UNIVERSITY MALAYA 0.42 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.33 RUMAH API TANJONG. 0.44 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.55 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.43 4.44 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.44 4.48 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.43 4.40 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.44 4.49 PUSAT PERTANIAN 0.44 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.54 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.44 4.55 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.45 4.61 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.45 4.59 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.45 4.66 FELDA BUKIT ROKAN 0.45 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 4.62 LADANG BATANG JELAI 0.44 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.57 CHEMARA RES. TANAH 0.43 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.47 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.43 4.45 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.44 4.48 LADANG AYER HITAM 0.44 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.57 RUMAH API PULAU Malaya0.44 111 46203 UNDAN MELAKA 102.3333 2.0500 53 4.51 ALAM (AKEDEMI LAUT 0.45 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.57 113 46206 MERLIMAU ESTATE MELAKAof 102.4333 2.1833 0 0.44 4.55 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.44 4.53 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.45 4.57 CHEMAR RES. SERKAM 0.44 116 46211 JASIN MELAKA 102.4167 2.3228 27 4.56 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.44 4.53 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.44 4.56 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.44 4.56 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.45 4.58 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.41 4.22 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.45 4.69 RPS KUALA BETIS 0.46 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 4.77 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.40 4.19 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.46 4.84 University 0.41 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 4.30 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.44 4.56 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.45 4.68 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.46 4.83 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.41 4.26 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.46 4.77 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.46 4.84 P. TER. HAIWAN TANAH 0.47 133 40546 MERAH KELANTAN 102.0092 5.8111 0 4.97 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.47 4.95 PEJ.HAI. JAJAHAN 0.47 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 4.93

168

136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.47 4.94 137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.48 5.01 PUSAT PERTANIAN 0.48 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.03 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.48 4.98 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.48 5.02 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.48 5.01 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.44 4.60 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.46 4.75 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.44 4.56 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.45 4.66 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.44 4.60 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.46 4.78 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.45 4.63 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.45 4.63 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.44 4.59 LADANG KOKO LANDAS 0.46 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 4.79 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.46 4.80 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.47 4.86 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.46 4.78 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.47 4.85 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.47 4.87 INSTITUT PERTANIAN Malaya0.48 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 4.97 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 0.46 4.76 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.45 4.69 of 0.46 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 4.74 PUSAT LATIHAN JHEOA 0.45 161 42304 Km 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.70 PERBADANAN KEMAJUAN 0.37 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 3.79 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.44 4.60 TALIKOM BUKIT 0.37 164 42321 PENINJAU (FRASER HILL) PAHANG 101.7500 3.7167 1324 3.79 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.46 4.71 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.45 4.66 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.45 4.67 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.46 4.80 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.45 4.66 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.43 4.45 TAPAK SEMAIAN LENTANG, BENTONG, 0.43 171 University42337 PAHANG PAHANG 101.8867 3.3900 153 4.48 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.46 4.79 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.45 4.60 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.46 4.78 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.43 4.45 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.47 4.92 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.44 4.58 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.46 4.82 PST. PERTANIAN 0.46 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 4.76 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.45 4.68 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.46 4.71

169

182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.46 4.71 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.45 4.66 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.45 4.67 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.46 4.70 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.46 4.71 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.45 4.63 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.45 4.65 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.45 4.70 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.45 4.66 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.46 4.72 PUSAT PERTANIAN JAMBU 0.45 192 42338 RIAS PAHANG 102.1500 3.4667 0 4.69 FELDA KAMPONG NEW 0.45 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.71 FELDA JENDERAK 0.46 194 42340 SELATAN PAHANG 102.3000 3.6833 0 4.77 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.46 4.74 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.46 4.70 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.46 4.74 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.46 4.76 FELDA SUNGAI PANCING 0.44 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.61 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.46 4.74 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.45 4.65 P.P.TANAMAN KAMPUNG 0.46 202 42360 AWAH PAHANG 102.5000Malaya 3.5833 31 4.72 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.46 4.73 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.45 4.69 PUSAT PERTANIAN BUKIT 0.45 206 42381 GOH PAHANGof 103.0833 3.8333 31 4.64 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.46 4.75 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.47 4.84 LADANG FELDA KECHAU 0.47 209 42415 06 PAHANG 102.1667 4.2500 0 4.90 FELDA SUNGAI KOYAN 0.47 210 42416 SATU PAHANG 101.8667 4.2667 0 4.92 MARDI CAMERON 0.37 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 3.86 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.44 4.56 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.39 4.00 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.38 3.93 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.40 4.10 JKR BANDAR KOTA 0.40 216 47121 TINGGI JOHORE 103.9000 1.7333 33 4.09 RUMAH API BKT. 0.42 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.27 University 0.42 218 47159 SINDORA JOHORE 103.4711 1.9636 81 4.25 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.44 4.49 KLINIK KESIHATAN 0.44 220 47203 KAHANG JOHORE 103.5333 2.2167 53 4.48 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.38 3.92 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.44 4.53 PEJ. KEJORA BANDAR 0.39 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 3.96 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.45 4.58 MARDI ALOR BUKIT 0.39 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 3.99 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.38 3.92 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.41 4.15

170

228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.39 4.02 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.40 4.12 PUSAT PERT. PARIT 0.41 230 47130 BOTAK JOHORE 103.0833 1.7167 2 4.19 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.41 4.19 47137 KULIM MONTEL FARM 0.39 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.01 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.43 4.39 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.40 4.11 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.40 4.08 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.40 4.11 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.44 4.47 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.42 4.29 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.41 4.24 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.42 4.33 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.43 4.45 FELDA TENGGAROH 0.43 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 4.41 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.43 4.41 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.43 4.42 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.43 4.43 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.44 4.52 FELDA BUKIT 0.45 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 4.58 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667Malaya 2.3833 0 0.45 4.60 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 0.46 4.70 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.45 4.62 251 47236 GOMALI ESTATE JOHOREof 102.6667 2.6167 0 0.45 4.65 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.46 4.75 253 95112 ARC SEMONGOK SARAWAK 110.3000 1.4000 62 0.34 3.48

University

171

Table A9 Solar radiation and clearness index prediction for month September.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.47 4.74 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.47 4.70 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.48 4.79 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.48 4.77 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.48 4.77 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.48 4.84 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.48 4.78 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.48 4.81 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.47 4.74 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.49 4.87 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.48 4.81 PUSAT PERT.BATU 0.48 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 4.78 MADA KOTA SARANG 0.47 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 4.74 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.48 4.76 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.45 4.50 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.45 4.46 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.47 4.67 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.48 4.76 Malaya0.48 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333 6.1667 15 4.79 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.47 4.67 MADA TELAGA BATU, 0.47 21 41615 JITRA KEDAHof 100.3500 6.2333 5 4.74 22 41622 FELDA BATU LAPAN KEDAH 100.5367 6.4167 0 0.48 4.82 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.48 4.80 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.47 4.74 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.47 4.66 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.40 3.96 KEBUN BUNGA PULAU 0.46 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.60 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.48 4.77 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.48 4.76 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.49 4.89 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.49 4.89 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.49 4.90 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.49 4.92 SEISMO. STN BUKIT University 0.48 34 43418 KLEDANG PERAK 101.0167 4.5833 240 4.76 HOSPITAL BAHAGIA ULU 0.49 35 43419 KINTA PERAK 101.1667 4.6667 70 4.95 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.47 4.66 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.49 4.94 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.40 4.04 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.49 4.89 HOSPITAL KUALA 0.49 40 43447 KANGSAR PERAK 100.9333 4.7667 68 4.90 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.48 4.83 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.48 4.85 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.49 4.87

172

44 43507 POS POI PERAK 101.2167 5.1000 1524 0.37 3.67 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.49 4.87 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.48 4.76 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.48 4.81 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.47 4.75 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.48 4.76 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.45 4.49 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.46 4.59 52 43353 PUSAT PERT. TITI GANTONG PERAK 100.8500 4.3667 0 0.49 4.87 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.47 4.71 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.48 4.78 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.47 4.71 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.47 4.74 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.47 4.72 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.49 4.90 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.46 4.59 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.49 4.89 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.50 4.99 CHERSONESE ESTATE 0.50 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 4.97 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.51 5.06 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.45 4.51 AMPANGAN AIR SG. Malaya 0.45 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.51 RUMAH API ONE FATHOM 0.42 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.25 MARINE DEPT. PORT 0.43 67 44298 KLANG SELANGORof 101.3833 3.0167 2 4.35 NEB CONNAUGHT BRIDGE 0.44 68 44305 POWER STATION KLANG SELANGOR 101.4667 3.0500 3 4.38 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.45 4.46 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.44 4.39 HOSPITAL KUALA KUBU 0.46 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.62 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.45 4.52 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.45 4.53 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.45 4.46 75 44255 PUSAT PERT. TELOK DATOK SELANGOR 101.5158 2.8167 0 0.44 4.43 BANTING OIL PALM RES. 0.44 76 44256 STN SELANGOR 101.5000 2.8167 0 4.43 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.44 4.43 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.44 4.39 79 University44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.44 4.41 PUSAT PERTANIAN 0.46 80 44315 BATANG KALI SELANGOR 101.6408 3.4797 46 4.57 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.44 4.40 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.44 4.39 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.48 4.78 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.46 4.59 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.44 4.36 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.45 4.47 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.44 4.37 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.44 4.39

173

89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.44 4.42 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.44 4.45 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.45 4.47 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.44 4.44 UNIVERSITY MALAYA 0.44 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.38 RUMAH API TANJONG. 0.47 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.66 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.45 4.53 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.45 4.55 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.45 4.47 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.46 4.59 PUSAT PERTANIAN 0.46 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.61 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.46 4.61 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.46 4.65 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.46 4.64 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.47 4.68 FELDA BUKIT ROKAN 0.46 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 4.65 LADANG BATANG JELAI 0.46 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.61 CHEMARA RES. TANAH 0.46 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.55 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.45 4.52 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.45 4.54 LADANG AYER HITAM Malaya 0.46 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.61 111 46203 RUMAH API PULAU UNDAN MELAKA 102.3333 2.0500 53 0.46 4.64 ALAM (AKEDEMI LAUT 0.47 112 46219 MALAYSIA) MELAKAof 102.0500 2.3667 0 4.67 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.46 4.63 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.46 4.62 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.46 4.64 CHEMAR RES. SERKAM 0.46 116 46211 JASIN MELAKA 102.4167 2.3228 27 4.63 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.46 4.62 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.47 4.67 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.46 4.62 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.47 4.65 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.45 4.51 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.49 4.90 RPS KUALA BETIS 0.50 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 4.97 124 University40433 POS HAU KELANTAN 101.5333 4.7000 655 0.45 4.49 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.50 5.01 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.46 4.58 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.48 4.81 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.49 4.91 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.50 5.03 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.45 4.53 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.50 5.00 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.50 5.03 P. TER. HAIWAN TANAH 0.52 133 40546 MERAH KELANTAN 102.0092 5.8111 0 5.16 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.51 5.14

174

PEJ.HAI. JAJAHAN 0.51 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 5.13 136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.51 5.14 137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.52 5.19 PUSAT PERTANIAN 0.52 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 5.20 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.52 5.17 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.52 5.19 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.52 5.19 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.46 4.64 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.49 4.89 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.46 4.61 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.47 4.69 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.47 4.66 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.48 4.84 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.47 4.69 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.47 4.69 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.46 4.64 LADANG KOKO LANDAS 0.48 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 4.85 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.49 4.86 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.49 4.95 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.48 4.84 155 49512 HAIWAN TERSAT TERENGGANU Malaya103.0500 5.0500 0 0.49 4.93 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.50 4.98 INSTITUT PERTANIAN 0.51 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 5.14 158 42249 JHEOA KUALA ROMPIN PAHANGof 103.5000 2.8167 0 0.48 4.80 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.47 4.70 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.48 4.77 PUSAT LATIHAN JHEOA Km 0.47 161 42304 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.75 PERBADANAN KEMAJUAN 0.39 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 3.86 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.46 4.64 TALIKOM BUKIT PENINJAU 0.38 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 3.83 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.47 4.74 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.47 4.72 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.47 4.70 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.49 4.92 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.47 4.68 170 University42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.45 4.50 TAPAK SEMAIAN LENTANG, 0.45 171 42337 BENTONG, PAHANG PAHANG 101.8867 3.3900 153 4.53 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.48 4.83 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.47 4.68 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.49 4.89 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.47 4.68 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.50 5.03 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.48 4.80 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.50 4.96 PST. PERTANIAN 0.48 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 4.80

175

180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.47 4.70 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.47 4.74 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.47 4.74 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.47 4.68 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.47 4.72 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.47 4.74 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.47 4.75 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.47 4.67 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.47 4.71 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.48 4.76 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.47 4.74 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.48 4.77 PUSAT PERTANIAN JAMBU 0.47 192 42338 RIAS PAHANG 102.1500 3.4667 0 4.72 FELDA KAMPONG NEW 0.48 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.77 194 42340 FELDA JENDERAK SELATAN PAHANG 102.3000 3.6833 0 0.48 4.81 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.48 4.79 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.47 4.73 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.48 4.76 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.48 4.82 FELDA SUNGAI PANCING 0.47 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.68 200 42351 FELDA JENGKA 14 PAHANG Malaya102.5500 3.7333 50 0.48 4.80 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.47 4.71 P.P.TANAMAN KAMPUNG 0.48 202 42360 AWAH PAHANG 102.5000 3.5833 31 4.77 203 42363 FELDA PPP TUN RAZAK PAHANGof 102.5667 3.8333 76 0.48 4.82 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.48 4.80 PUSAT PERTANIAN BUKIT 0.47 206 42381 GOH PAHANG 103.0833 3.8333 31 4.72 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.48 4.85 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.49 4.92 209 42415 LADANG FELDA KECHAU 06 PAHANG 102.1667 4.2500 0 0.50 4.99 FELDA SUNGAI KOYAN 0.50 210 42416 SATU PAHANG 101.8667 4.2667 0 5.00 MARDI CAMERON 0.38 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 3.82 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.46 4.62 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.41 4.12 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.40 4.03 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.42 4.16 216 University47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.42 4.15 RUMAH API BKT. 0.44 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.40 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.43 4.32 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.46 4.57 KLINIK KESIHATAN 0.45 220 47203 KAHANG JOHORE 103.5333 2.2167 53 4.50 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.40 4.04 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.45 4.55 PEJ. KEJORA BANDAR 0.40 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 4.03 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.46 4.60 MARDI ALOR BUKIT 0.41 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.11

176

226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.40 4.02 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.42 4.23 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.41 4.08 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.42 4.16 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.43 4.30 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.43 4.26 47137 KULIM MONTEL FARM 0.41 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 4.09 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.45 4.49 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.42 4.20 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.42 4.17 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.42 4.19 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.45 4.48 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.44 4.36 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.43 4.32 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.44 4.39 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.45 4.54 FELDA TENGGAROH TIMUR 0.44 242 47209 2 JOHORE 103.8667 2.0833 25 4.43 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.44 4.45 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.45 4.47 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.45 4.50 246 47215 FELDA LENGA JOHORE Malaya102.8500 2.2500 36 0.46 4.56 247 47220 FELDA BUKIT SERAMPANG JOHORE 102.8000 2.3333 0 0.46 4.61 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.46 4.62 249 47229 FELCRA TANAH ABANG JOHOREof 103.6489 2.4753 0 0.47 4.72 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.46 4.64 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.47 4.67 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.48 4.78 253 95112 ARC SEMONGOK SARAWAK 110.3000 1.4000 62 0.34 3.40

University

177

Table A10 Solar radiation and clearness index prediction for month October.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.47 4.68 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.47 4.67 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.46 4.64 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.46 4.63 HOSPITAL SUNGAI 0.46 5 41543 PETANI KEDAH 100.5000 5.6500 8 4.64 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.47 4.65 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.47 4.68 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.47 4.65 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.46 4.57 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.47 4.70 P. PERT. CHAROK 0.47 11 41548 PADANG KEDAH 100.7167 5.8000 31 4.65 PUSAT PERT.BATU 0.46 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 4.61 MADA KOTA SARANG 0.46 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 4.64 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.46 4.63 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.45 4.52 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.45 4.48 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.46 4.62 18 41610 MARDI BUKIT RAYA KEDAH 100.4500Malaya 6.0167 8 0.47 4.65 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333 6.1667 15 0.47 4.68 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.46 4.56 MADA TELAGA BATU, 0.47 21 41615 JITRA KEDAHof 100.3500 6.2333 5 4.66 22 41622 FELDA BATU LAPAN KEDAH 100.5367 6.4167 0 0.48 4.73 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.48 4.73 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.47 4.65 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.46 4.58 PUSAT KES. BKT. 0.40 26 41533 BENDERA P. PINANG 100.2667 5.4167 732 3.99 KEBUN BUNGA PULAU 0.45 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.51 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.46 4.64 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.46 4.63 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.47 4.77 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.47 4.77 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.47 4.77 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.48 4.78 SEISMO. STN BUKIT University 0.46 34 43418 KLEDANG PERAK 101.0167 4.5833 240 4.60 HOSPITAL BAHAGIA ULU 0.48 35 43419 KINTA PERAK 101.1667 4.6667 70 4.80 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.44 4.47 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.48 4.78 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.39 3.94 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.47 4.74 HOSPITAL KUALA 0.47 40 43447 KANGSAR PERAK 100.9333 4.7667 68 4.74 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.47 4.69 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.47 4.68 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.47 4.68

178

44 43507 POS POI PERAK 101.2167 5.1000 1524 0.35 3.53 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.47 4.67 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.45 4.55 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.47 4.67 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.45 4.54 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.45 4.52 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.43 4.30 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.44 4.48 PUSAT PERT. TITI 0.47 52 43353 GANTONG PERAK 100.8500 4.3667 0 4.73 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.45 4.58 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.46 4.66 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.45 4.58 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.46 4.62 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.45 4.58 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.47 4.76 MARDI HILIR PERAK 0.44 59 43426 (LKM) PERAK 100.8667 3.8833 9 4.47 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.47 4.74 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.48 4.83 CHERSONESE ESTATE 0.48 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 4.79 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.48 4.85 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.44 4.52 AMPANGAN AIR SG. Malaya 0.44 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.48 RUMAH API ONE FATHOM 0.42 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.28 MARINE DEPT. PORT 0.43 67 44298 KLANG SELANGORof 101.3833 3.0167 2 4.34 NEB CONNAUGHT BRIDGE POWER STATION 0.43 68 44305 KLANG SELANGOR 101.4667 3.0500 3 4.35 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.43 4.38 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.42 4.29 HOSPITAL KUALA KUBU 0.45 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.52 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.44 4.45 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.45 4.55 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.44 4.42 PUSAT PERT. TELOK 0.44 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.45 BANTING OIL PALM RES. 0.44 76 44256 STN SELANGOR 101.5000 2.8167 0 4.44 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.43 4.39 78 University44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.43 4.37 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.43 4.34 PUSAT PERTANIAN 0.44 80 44315 BATANG KALI SELANGOR 101.6408 3.4797 46 4.48 MARDI TANJONG 0.43 81 44325 KARANG SELANGOR 101.1564 3.4547 2 4.33 PUSAT PERT.TJG. 0.43 82 44326 KARANG SELANGOR 101.1833 3.4167 0 4.32 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.46 4.69 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.44 4.49 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.43 4.32 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.43 4.39 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.43 4.34

179

88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.43 4.33 SELANGOR RIVER 0.43 89 44362 ESTATE SELANGOR 101.3333 3.3500 0 4.35 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.43 4.37 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.44 4.41 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.43 4.39 UNIVERSITY MALAYA 0.43 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.33 RUMAH API TANJONG. 0.46 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.71 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.44 4.52 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.44 4.50 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.43 4.41 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.45 4.61 PUSAT PERTANIAN 0.45 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.58 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.45 4.58 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.45 4.59 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.45 4.58 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.45 4.61 FELDA BUKIT ROKAN 0.45 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 4.61 LADANG BATANG JELAI 0.45 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.55 CHEMARA RES. TANAH 0.45 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.58 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.44 4.51 Malaya0.45 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 4.54 LADANG AYER HITAM 0.45 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.53 RUMAH API PULAU 0.46 111 46203 UNDAN MELAKAof 102.3333 2.0500 53 4.67 ALAM (AKEDEMI LAUT 0.46 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.71 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.46 4.65 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 0.45 4.64 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.46 4.64 CHEMAR RES. SERKAM 0.45 116 46211 JASIN MELAKA 102.4167 2.3228 27 4.63 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.45 4.61 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.46 4.71 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.45 4.61 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.46 4.66 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.42 4.26 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.47 4.73 RPS KUALA BETIS 0.48 123 University40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 4.81 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.42 4.22 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.48 4.79 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.43 4.33 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.46 4.60 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.47 4.68 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.48 4.81 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.42 4.24 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.47 4.68 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.48 4.79 P. TER. HAIWAN TANAH 0.48 133 40546 MERAH KELANTAN 102.0092 5.8111 0 4.83

180

134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.48 4.75 PEJ.HAI. JAJAHAN 0.48 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 4.77 P. PERT. BATANG 0.48 136 40587 MERBAU KELANTAN 102.0500 5.8167 21 4.79 PUSAT PERTANIAN 0.48 137 40588 MELOR KELANTAN 102.3000 5.9667 7 4.79 PUSAT PERTANIAN 0.48 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 4.78 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.48 4.80 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.48 4.79 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.48 4.78 BALAI PENGHULU 0.44 142 49411 KERTIH TERENGGANU 103.4333 4.5167 0 4.43 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.47 4.68 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.44 4.46 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.44 4.42 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.45 4.55 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.45 4.57 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.45 4.56 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.45 4.56 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.44 4.48 LADANG KOKO LANDAS 0.45 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 4.57 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.45 4.57 PERTANIAN PADANG 0.46 153 49503 IPOH TERENGGANU 103.0167 5.0500 0 4.65 Malaya0.45 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 4.52 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.46 4.63 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.46 4.60 INSTITUT PERTANIAN of 0.48 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 4.75 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 0.46 4.64 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.45 4.62 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.46 4.64 PUSAT LATIHAN JHEOA 0.46 161 42304 Km 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.67 PERBADANAN 0.35 162 42318 KEMAJUAN BKT. FRASER PAHANG 101.7333 3.7167 1280 3.57 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.45 4.54 TALIKOM BUKIT 0.35 164 42321 PENINJAU (FRASER HILL) PAHANG 101.7500 3.7167 1324 3.54 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.46 4.65 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.46 4.62 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.45 4.59 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.48 4.81 University 0.45 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 4.60 P PERIKANAN BKT. 0.43 170 42332 TINGGI PAHANG 101.8167 3.3667 153 4.40 TAPAK SEMAIAN LENTANG, BENTONG, 0.44 171 42337 PAHANG PAHANG 101.8867 3.3900 153 4.43 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.47 4.75 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.45 4.58 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.47 4.78 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.44 4.48 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.49 4.91 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.46 4.62

181

178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.48 4.84 PST. PERTANIAN 0.45 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 4.63 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.45 4.61 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.46 4.65 182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.46 4.66 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.45 4.60 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.46 4.63 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.45 4.62 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.46 4.64 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.45 4.58 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.46 4.61 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.46 4.67 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.46 4.62 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.46 4.68 PUSAT PERTANIAN 0.46 192 42338 JAMBU RIAS PAHANG 102.1500 3.4667 0 4.63 FELDA KAMPONG NEW 0.46 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.69 FELDA JENDERAK 0.47 194 42340 SELATAN PAHANG 102.3000 3.6833 0 4.74 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.47 4.71 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.45 4.62 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.46 4.68 198 42345 FELDA ULU JEMPOL PAHANG 102.6333Malaya 3.7500 0 0.47 4.74 FELDA SUNGAI PANCING 0.45 199 42349 SELATAN PAHANG 103.1667 3.8000 50 4.58 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.47 4.72 201 42354 FELDA BUKIT KUANTAN PAHANGof 103.1500 3.9167 0 0.46 4.61 P.P.TANAMAN KAMPUNG 0.46 202 42360 AWAH PAHANG 102.5000 3.5833 31 4.69 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.47 4.72 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.46 4.68 PUSAT PERTANIAN BUKIT 0.46 206 42381 GOH PAHANG 103.0833 3.8333 31 4.62 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.47 4.74 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.48 4.84 LADANG FELDA KECHAU 0.49 209 42415 06 PAHANG 102.1667 4.2500 0 4.90 FELDA SUNGAI KOYAN 0.49 210 42416 SATU PAHANG 101.8667 4.2667 0 4.91 MARDI CAMERON 0.36 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 3.58 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.44 4.49 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.40 4.06 214 University47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.38 3.93 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.39 4.02 JKR BANDAR KOTA 0.39 216 47121 TINGGI JOHORE 103.9000 1.7333 33 4.01 RUMAH API BKT. 0.43 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.38 218 47159 SINDORA JOHORE 103.4711 1.9636 81 0.41 4.19 PUSAT BUKIT PASIR 0.45 219 47202 MUAR JOHORE 102.6333 2.1000 11 4.56 KLINIK KESIHATAN 0.43 220 47203 KAHANG JOHORE 103.5333 2.2167 53 4.33 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.39 3.96 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.43 4.38 223 47207 PEJ. KEJORA BANDAR JOHORE 104.2333 1.5500 35 0.38 3.87

182

PENAWAR

224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.44 4.50 MARDI ALOR BUKIT 0.40 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.04 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.38 3.90 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.40 4.09 228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.38 3.92 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.39 3.98 PUSAT PERT. PARIT 0.42 230 47130 BOTAK JOHORE 103.0833 1.7167 2 4.26 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.41 4.15 47137 KULIM MONTEL FARM 0.39 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 3.95 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.44 4.50 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.40 4.11 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.40 4.04 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.40 4.06 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.42 4.27 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.42 4.28 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.41 4.21 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.42 4.31 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.44 4.52 FELDA TENGGAROH 0.41 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 4.23 243 47211 CEP NIYOR ESTATE JOHORE 103.2500Malaya 2.0833 0 0.43 4.35 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.43 4.37 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.44 4.46 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.44 4.51 FELDA BUKIT of 0.45 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 4.56 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.45 4.56 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 0.44 4.52 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.45 4.55 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.45 4.61 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.45 4.60 253 95112 ARC SEMONGOK SARAWAK 110.3000 1.4000 62 0.33 3.40

University

183

Table A11 Solar radiation and clearness index prediction for month November.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.49 4.62 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.49 4.57 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.49 4.68 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.49 4.67 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.50 4.72 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.49 4.66 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.50 4.69 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.48 4.58 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.47 4.47 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.50 4.77 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.49 4.70 12 41549 PUSAT PERT.BATU SEKETOL KEDAH 100.8000 5.9667 71 0.48 4.59 MADA KOTA SARANG 0.50 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 4.71 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.49 4.66 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.48 4.56 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.48 4.50 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.49 4.68 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.50 4.71 19 41611 PERTANIAN GAJAH MATI KEDAH Malaya100.5333 6.1667 15 0.50 4.69 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.48 4.51 21 41615 MADA TELAGA BATU, JITRA KEDAH 100.3500 6.2333 5 0.50 4.69 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.50 4.68 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.49 4.66 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.48 4.55 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.49 4.64 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.39 3.73 KEBUN BUNGA PULAU 0.48 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.55 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.49 4.71 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.44 4.31 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.46 4.46 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.46 4.46 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.46 4.49 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.47 4.57 SEISMO. STN BUKIT 0.45 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 4.37 HOSPITAL BAHAGIA ULU 0.48 35 43419 KINTA PERAK 101.1667 4.6667 70 4.61 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.43 4.14 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.48 4.59 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.37 3.59 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.49 4.67 40 43447 HOSPITAL KUALA KANGSAR PERAK 100.9333 4.7667 68 0.48 4.62 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.49 4.67 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.48 4.60 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.47 4.51

184

44 43507 POS POI PERAK 101.2167 5.1000 1524 0.36 3.43 45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.48 4.56 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.45 4.28 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.49 4.68 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.46 4.36 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.45 4.29 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.42 4.04 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.43 4.14 52 43353 PUSAT PERT. TITI GANTONG PERAK 100.8500 4.3667 0 0.47 4.50 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.44 4.25 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.44 4.29 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.44 4.26 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.44 4.29 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.44 4.30 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.47 4.55 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.43 4.15 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.48 4.62 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.49 4.75 CHERSONESE ESTATE KUALA 0.48 62 43500 KURAU PERAK 101.4333 5.0000 94 4.59 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.48 4.59 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.44 4.31 AMPANGAN AIR SG. Malaya 0.43 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.20 RUMAH API ONE FATHOM 0.42 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.13 67 44298 MARINE DEPT. PORT KLANG SELANGORof 101.3833 3.0167 2 0.42 4.10 NEB CONNAUGHT BRIDGE 0.42 68 44305 POWER STATION KLANG SELANGOR 101.4667 3.0500 3 4.10 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.41 4.04 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.41 3.97 HOSPITAL KUALA KUBU 0.42 71 44322 BARU SELANGOR 101.6500 3.5667 61 4.12 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.42 4.10 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.44 4.37 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.42 4.16 75 44255 PUSAT PERT. TELOK DATOK SELANGOR 101.5158 2.8167 0 0.43 4.24 76 44256 BANTING OIL PALM RES. STN SELANGOR 101.5000 2.8167 0 0.43 4.24 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.42 4.12 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.42 4.13 79 University44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.41 4.03 PUSAT PERTANIAN BATANG 0.42 80 44315 KALI SELANGOR 101.6408 3.4797 46 4.10 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.41 4.02 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.41 4.01 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.44 4.24 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.42 4.10 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.41 4.06 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.41 4.05 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.42 4.08 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.41 4.03 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.41 4.04

185

90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.41 4.04 91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.42 4.10 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.42 4.09 UNIVERSITY MALAYA 0.41 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 4.04 95 45224 RUMAH API TANJONG. TUAN N. SEMBILAN 101.8500 2.4000 0 0.46 4.58 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.44 4.30 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.43 4.24 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.42 4.11 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.45 4.44 PUSAT PERTANIAN 0.44 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.36 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.44 4.36 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.44 4.29 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.44 4.29 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.43 4.26 105 45242 FELDA BUKIT ROKAN UTARA N. SEMBILAN 102.4167 2.6667 0 0.44 4.32 LADANG BATANG JELAI 0.43 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 4.25 CHEMARA RES. TANAH 0.45 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.39 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.44 4.29 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.44 4.31 LADANG AYER HITAM 0.42 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 4.17 111 46203 RUMAH API PULAU UNDAN MELAKA Malaya102.3333 2.0500 53 0.47 4.62 ALAM (AKEDEMI LAUT 0.46 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.56 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.46 4.51 of 0.46 114 46207 DEVON ESTATE MELAKA 102.4167 2.2000 42 4.51 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.45 4.47 116 46211 CHEMAR RES. SERKAM JASIN MELAKA 102.4167 2.3228 27 0.45 4.46 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.45 4.45 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.46 4.58 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.45 4.41 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.46 4.49 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.40 3.81 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.45 4.33 123 40432 RPS KUALA BETIS (LAMBOK) KELANTAN 101.7500 4.7000 153 0.46 4.44 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.39 3.77 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.44 4.20 126 University40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.41 3.89 127 40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.44 4.25 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.45 4.31 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.47 4.50 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.40 3.80 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.45 4.33 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.45 4.29 P. TER. HAIWAN TANAH 0.47 133 40546 MERAH KELANTAN 102.0092 5.8111 0 4.48 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.44 4.17 135 40586 PEJ.HAI. JAJAHAN MACHANG KELANTAN 102.2000 5.7667 31 0.45 4.28

186

136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.46 4.39 137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.44 4.22 138 40660 PUSAT PERTANIAN BACHOK KELANTAN 102.4000 6.0500 3 0.44 4.13 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.46 4.32 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.44 4.20 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.44 4.19 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.38 3.66 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.41 3.96 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.38 3.73 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.38 3.62 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.40 3.85 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.39 3.75 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.40 3.85 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.40 3.83 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.39 3.74 LADANG KOKO LANDAS 0.39 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 3.75 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.39 3.75 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.40 3.85 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.39 3.70 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.40 3.83 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.40 3.79 Malaya0.43 157 49539 INSTITUT PERTANIAN BESUT TERENGGANU 102.6167 5.6500 10 4.05 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 0.42 4.17 159 42251 POS ISKANDAR PAHANGof 102.6500 3.0333 0 0.43 4.18 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.43 4.18 PUSAT LATIHAN JHEOA Km 0.42 161 42304 56 (Bt. 34) PAHANG 102.9167 3.6000 0 4.08 PERBADANAN KEMAJUAN 0.35 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 3.43 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.42 4.11 TALIKOM BUKIT PENINJAU 0.35 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 3.42 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.43 4.15 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.42 4.06 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.42 4.16 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.46 4.41 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.43 4.16 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.41 4.03 TAPAK SEMAIAN LENTANG, 0.41 171 University42337 BENTONG, PAHANG PAHANG 101.8867 3.3900 153 4.03 172 42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.44 4.25 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.41 3.97 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.44 4.30 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.42 4.02 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.48 4.65 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.43 4.20 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.46 4.46 PST. PERTANIAN 0.43 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 4.18 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.43 4.20 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.43 4.18

187

182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.43 4.15 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.43 4.16 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.42 4.07 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.42 4.12 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.42 4.12 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.42 4.13 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.43 4.15 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.42 4.10 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.43 4.17 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.43 4.15 PUSAT PERTANIAN JAMBU 0.43 192 42338 RIAS PAHANG 102.1500 3.4667 0 4.18 FELDA KAMPONG NEW 0.42 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 4.10 194 42340 FELDA JENDERAK SELATAN PAHANG 102.3000 3.6833 0 0.43 4.23 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.43 4.20 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.42 4.14 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.43 4.23 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.43 4.18 FELDA SUNGAI PANCING 0.40 199 42349 SELATAN PAHANG 103.1667 3.8000 50 3.91 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.43 4.15 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.41 3.94 P.P.TANAMAN KAMPUNG 0.43 202 42360 AWAH PAHANG Malaya102.5000 3.5833 31 4.16 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.43 4.14 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.42 4.06 PUSAT PERTANIAN BUKIT 0.41 206 42381 GOH PAHANGof 103.0833 3.8333 31 3.96 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.44 4.30 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.44 4.31 209 42415 LADANG FELDA KECHAU 06 PAHANG 102.1667 4.2500 0 0.46 4.44 210 42416 FELDA SUNGAI KOYAN SATU PAHANG 101.8667 4.2667 0 0.47 4.52 MARDI CAMERON 0.36 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 3.44 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.39 3.78 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.42 4.15 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.40 4.01 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.40 3.99 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.40 3.97 217 47139 RUMAH API BKT. SEGENTING JOHORE 102.8833 1.8000 77 0.44 4.38 218 University47159 SINDORA JOHORE 103.4711 1.9636 81 0.41 4.11 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.45 4.45 220 47203 KLINIK KESIHATAN KAHANG JOHORE 103.5333 2.2167 53 0.42 4.12 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.41 4.05 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.42 4.14 PEJ. KEJORA BANDAR 0.39 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 3.86 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.43 4.25 MARDI ALOR BUKIT 0.41 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 4.12 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.40 3.96 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.41 4.05

188

228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.39 3.91 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.39 3.91 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.43 4.28 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.42 4.12 47137 KULIM MONTEL FARM 0.40 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 3.96 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.45 4.45 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.42 4.13 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.41 4.04 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.41 4.03 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.41 4.04 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.42 4.21 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.42 4.13 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.43 4.22 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.45 4.42 242 47209 FELDA TENGGAROH TIMUR 2 JOHORE 103.8667 2.0833 25 0.41 4.04 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.43 4.21 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.43 4.22 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.44 4.35 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.44 4.33 247 47220 FELDA BUKIT SERAMPANG JOHORE 102.8000 2.3333 0 0.44 4.34 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.44 4.31 Malaya0.42 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 4.18 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.43 4.26 251 47236 GOMALI ESTATE JOHOREof 102.6667 2.6167 0 0.44 4.30 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 0.42 4.18

University

189

Table A12 Solar radiation and clearness index prediction for month December.

NO ID STATION NAME STATE Longitude Latitude Alt. KT Predication

1 41634 FELDA CHUPING (B) PERLIS 100.3481 6.5036 15 0.47 4.29 2 41636 TAMAN NEGERI PERLIS PERLIS 100.2000 6.7000 0 0.47 4.22 3 41522 BUKIT SIDIM KEDAH 100.6667 5.4833 39 0.50 4.62 4 41540 HOSPITAL KULIM KEDAH 100.5500 5.3833 32 0.51 4.67 5 41543 HOSPITAL SUNGAI PETANI KEDAH 100.5000 5.6500 8 0.51 4.70 6 41545 HOSPITAL BALING KEDAH 100.9167 5.6833 52 0.49 4.45 7 41612 P PEMBIAKAN IKAN JITRA KEDAH 100.4667 6.2500 10 0.49 4.44 8 41619 AMPANGAN PEDU KEDAH 100.7667 6.2500 59 0.46 4.19 9 41638 AMPANGAN MUDA KEDAH 100.8500 6.1167 110 0.45 4.09 10 41526 BADENOCH ESTATE KEDAH 100.7572 5.5514 0 0.51 4.70 11 41548 P. PERT. CHAROK PADANG KEDAH 100.7167 5.8000 31 0.50 4.54 PUSAT PERT.BATU 0.47 12 41549 SEKETOL KEDAH 100.8000 5.9667 71 4.31 MADA KOTA SARANG 0.51 13 41551 SEMUT, PENDANG KEDAH 100.3833 5.9667 5 4.63 14 41559 FELDA SUNGAI TIANG KEDAH 100.5950 5.9914 38 0.49 4.47 15 41603 LAMAN PADI LANGKAWI KEDAH 99.7225 6.2978 1 0.50 4.55 16 41604 MARDI LANGKAWI KEDAH 99.8000 6.3728 44 0.49 4.41 17 41606 P. PERT. TELUK CHENGAI KEDAH 100.1803 6.0975 1 0.51 4.61 18 41610 MARDI BUKIT RAYA KEDAH 100.4500 6.0167 8 0.50 4.58 19 41611 PERTANIAN GAJAH MATI KEDAH 100.5333Malaya 6.1667 15 0.49 4.46 20 41613 FELDA BUKIT TEMBAGA KEDAH 100.5356 6.2111 92 0.46 4.20 MADA TELAGA BATU, 0.50 21 41615 JITRA KEDAH 100.3500 6.2333 5 4.50 22 41622 FELDA BATU LAPAN KEDAHof 100.5367 6.4167 0 0.48 4.33 23 41624 FELDA BUKIT TANGGA KEDAH 100.4747 6.4906 0 0.47 4.30 24 41625 MARDI BUKIT TANGGA KEDAH 100.5000 6.4667 50 0.46 4.17 25 41523 MUKA HEAD (USM) P. PINANG 100.2000 5.4667 5 0.51 4.72 26 41533 PUSAT KES. BKT. BENDERA P. PINANG 100.2667 5.4167 732 0.39 3.55 KEBUN BUNGA PULAU 0.50 27 41536 PINANG P. PINANG 100.2833 5.4333 74 4.55 28 41525 MARDI BERTAM P. PINANG 100.4833 5.5500 7 0.51 4.72 29 43402 HOSPITAL TELUK INTAN PERAK 101.0167 4.0333 3 0.45 4.18 30 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 35 0.46 4.25 31 43413* HOSPITAL TAPAH PERAK 101.2667 4.2000 36 0.46 4.25 32 43414 HOSPITAL KAMPAR PERAK 101.1500 4.3000 38 0.46 4.32 33 43417 HOSPITAL BATU GAJAH PERAK 101.0333 4.4667 34 0.48 4.43 SEISMO. STN BUKIT 0.44 34 University43418 KLEDANG PERAK 101.0167 4.5833 240 4.12 HOSPITAL BAHAGIA ULU 0.48 35 43419 KINTA PERAK 101.1667 4.6667 70 4.42 36 43436 POS LANDAP (KUAH) PERAK 101.2667 4.7833 411 0.41 3.79 37 43437 POS LEGAP PERAK 101.2833 4.8500 107 0.47 4.33 38 43445 BUKIT LARUT PERAK 100.8000 4.8667 1037 0.38 3.48 39 43446 HOSPITAL TAIPING PERAK 100.7322 4.8519 18 0.50 4.64 HOSPITAL KUALA 0.49 40 43447 KANGSAR PERAK 100.9333 4.7667 68 4.50 41 43503 JKR BAGAN SERAI PERAK 100.5333 5.0167 3 0.51 4.71 42 43505 HOSPITAL LENGGONG PERAK 100.9667 5.1000 101 0.48 4.44 43 43506 POS PIAH (SULLEH) PERAK 101.2833 5.0667 153 0.45 4.19 44 43507 POS POI PERAK 101.2167 5.1000 1524 0.37 3.41

190

45 43508 STN. JANALETRIK BERSIA PERAK 101.2000 5.3000 110 0.46 4.28 46 43514 RPS KEMAR PERAK 101.3833 5.2000 250 0.42 3.91 47 43520 HOSPITAL PARIT BUNTAR PERAK 100.5000 5.1333 3 0.51 4.73 48 43561 RPS AYER DALA GRIK PERAK 101.2167 5.3833 200 0.44 4.02 49 43562 RPS AYER BANUN GRIK PERAK 101.3500 5.5333 200 0.43 3.91 50 43333 FELDA NENERING DUA PERAK 101.0667 5.6833 323 0.41 3.73 51 43347 FELDA SUNGAI BEHRANG PERAK 101.0333 3.7833 0 0.43 4.02 PUSAT PERT. TITI 0.47 52 43353 GANTONG PERAK 100.8500 4.3667 0 4.41 53 43356 NOVA SCOTIA ESTATE PERAK 100.9797 3.9697 0 0.44 4.13 54 43357 FELDA TROLAK UTARA PERAK 101.3667 3.9500 58 0.43 4.07 55 43395 RUBANA ESTATE PERAK 100.9667 3.9833 0 0.44 4.14 56 43401 SABRANG ESTATE PERAK 101.0000 4.0167 0 0.44 4.17 57 43404 PERTANIAN LEKIR PERAK 100.7467 4.1386 7 0.45 4.21 58 43416 MARDI PARIT PERAK 100.9000 4.4333 5 0.48 4.46 59 43426 MARDI HILIR PERAK (LKM) PERAK 100.8667 3.8833 9 0.43 4.05 60 43448 MARDI KUALA KANGSAR PERAK 100.9072 4.7639 66 0.49 4.51 61 43456 FELDA LASAH PERAK 101.0667 4.9333 0 0.50 4.64 CHERSONESE ESTATE 0.46 62 43500 KUALA KURAU PERAK 101.4333 5.0000 94 4.27 63 43517 FELDA IJOK PERAK 101.7667 5.1667 38 0.45 4.14 64 44242 LPA SALAK TINGGI SELANGOR 101.7500 2.7500 0 0.44 4.15 AMPANGAN AIR SG. 0.42 65 44252 SEMENYIH SELANGOR 101.8667 2.9333 0 4.01 RUMAH API ONE FATHOM Malaya 0.43 66 44258 BANK SELANGOR 101.0000 2.8833 21 4.11 MARINE DEPT. PORT 0.42 67 44298 KLANG SELANGOR 101.3833 3.0167 2 4.01 NEB CONNAUGHT BRIDGE 0.42 68 44305 POWER STATION KLANG SELANGORof 101.4667 3.0500 3 3.99 69 44316 JHEOA ULU GOMBAK SELANGOR 101.7333 3.2833 109 0.40 3.82 70 44320 AMPANGAN ULU LANGAT SELANGOR 101.8667 3.2167 233 0.39 3.71 HOSPITAL KUALA KUBU 0.41 71 44322 BARU SELANGOR 101.6500 3.5667 61 3.88 72 44354 KLANG GATE SELANGOR 101.7500 3.2333 0 0.41 3.91 73 44239 SEPANG ESTATE SELANGOR 101.7167 2.6833 0 0.44 4.22 74 44254 PORIM BANGI SELANGOR 101.7408 2.9678 24 0.42 3.99 PUSAT PERT. TELOK 0.44 75 44255 DATOK SELANGOR 101.5158 2.8167 0 4.14 BANTING OIL PALM RES. 0.44 76 44256 STN SELANGOR 101.5000 2.8167 0 4.15 77 44301 PUSAT PERT. SERDANG SELANGOR 101.7000 3.0000 44 0.42 3.96 78 44304 MARDI KLANG SELANGOR 101.4833 2.9833 3 0.42 4.03 79 44312 F.R.I. KEPONG (FRIM) SELANGOR 101.6333 3.2333 97 0.41 3.85 PUSAT PERTANIAN 0.41 80 University44315 BATANG KALI SELANGOR 101.6408 3.4797 46 3.88 81 44325 MARDI TANJONG KARANG SELANGOR 101.1564 3.4547 2 0.42 3.92 82 44326 PUSAT PERT.TJG. KARANG SELANGOR 101.1833 3.4167 0 0.41 3.92 83 44340 P.P. SUNGAI BESAR SELANGOR 101.9833 3.6667 0 0.42 3.92 84 44347 P.LAT.PERT. KALUMPANG SELANGOR 101.4833 3.6333 87 0.41 3.89 85 44352 JALAN ACOB ESTATE SELANGOR 101.3833 3.1333 0 0.42 3.96 86 44356 TENNAMARAN ESTATE SELANGOR 101.4167 3.4000 15 0.41 3.91 87 44360 BKT. RAJAH ESTATE SELANGOR 101.4333 3.0906 0 0.42 3.98 88 44361 SUNGAI BULUH ESTATE SELANGOR 101.3167 3.3000 0 0.41 3.92 89 44362 SELANGOR RIVER ESTATE SELANGOR 101.3333 3.3500 0 0.41 3.92 90 44363 RAJA MUSA ESTATE SELANGOR 101.3167 3.4167 0 0.42 3.92

191

91 44333 PARLIMENT K. LUMPUR 101.6667 3.1500 0 0.42 3.94 92 44334 TUDM SUNGAI BESI K. LUMPUR 101.6969 3.1078 33 0.41 3.93 UNIVERSITY MALAYA 0.41 93 48307 KUALA LUMPUR K. LUMPUR 101.6500 3.1167 104.0 3.87 RUMAH API TANJONG. 0.46 95 45224 TUAN N. SEMBILAN 101.8500 2.4000 0 4.40 96 45241 HOSPITAL SEREMBAN N. SEMBILAN 101.9436 2.7092 64 0.43 4.09 97 45244 HOSPITAL KUALA PILAH N. SEMBILAN 102.2500 2.7333 107 0.41 3.94 98 45248 HOSPITAL JELEBU N. SEMBILAN 102.0667 2.9500 137 0.40 3.84 99 45220 ATHERTON ESTATE N. SEMBILAN 101.9167 2.5500 40 0.44 4.24 PUSAT PERTANIAN 0.42 100 45231 GEMENCHEH N. SEMBILAN 102.3833 2.5167 70 4.04 101 45232 REGENT ESTATE N. SEMBILAN 102.4044 2.5139 64 0.42 4.04 102 45235 FELDA PASIR BESAR N. SEMBILAN 102.5500 2.6500 31 0.41 3.93 103 45237 HAIWAN JELAI GEMAS N. SEMBILAN 102.5458 2.6289 46 0.41 3.93 104 45238 FELDA PALONG LIMA N. SEMBILAN 102.6000 2.7500 0 0.41 3.90 FELDA BUKIT ROKAN 0.42 105 45242 UTARA N. SEMBILAN 102.4167 2.6667 0 4.00 LADANG BATANG JELAI 0.41 106 45245 ROMPIN N. SEMBILAN 102.4667 2.7000 67 3.91 CHEMARA RES. TANAH 0.44 107 45251 MERAH N. SEMBILAN 101.7875 2.6456 5 4.22 108 45253 NEW LABU ESTATE N. SEMBILAN 101.8000 2.7833 0 0.43 4.12 109 45254 KIRBY ESTATE N. SEMBILAN 101.8500 2.7500 0 0.43 4.13 LADANG AYER HITAM 0.40 110 45256 (GLANDALE) N. SEMBILAN 102.4167 2.9333 59 3.83 111 46203 RUMAH API PULAU UNDAN MELAKA 102.3333 2.0500 53 0.45 4.31 ALAM (AKEDEMI LAUT Malaya0.45 112 46219 MALAYSIA) MELAKA 102.0500 2.3667 0 4.33 113 46206 MERLIMAU ESTATE MELAKA 102.4333 2.1833 0 0.44 4.21 114 46207 DEVON ESTATE MELAKAof 102.4167 2.2000 42 0.44 4.20 115 46208 JASIN ESTATE MELAKA 102.4333 2.2833 0 0.44 4.17 CHEMAR RES. SERKAM 0.43 116 46211 JASIN MELAKA 102.4167 2.3228 27 4.15 117 46212 FELDA KEMENDOR MELAKA 102.4000 2.3500 69 0.43 4.12 118 46218 MARDI KUALA LINGGI MELAKA 102.0000 2.3667 8 0.45 4.35 119 46223 FELDA BKT. SENGGEH MELAKA 102.4667 2.3833 46 0.43 4.08 120 46225 FELDA HUTAN PERCHA MELAKA 102.3000 2.3667 0 0.44 4.20 121 40430 POS BROOKE KELANTAN 101.4833 4.6333 640 0.39 3.58 122 40431 POS BLAU KELANTAN 101.6833 4.6500 244 0.42 3.89 RPS KUALA BETIS 0.43 123 40432 (LAMBOK) KELANTAN 101.7500 4.7000 153 4.00 124 40433 POS HAU KELANTAN 101.5333 4.7000 655 0.38 3.57 125 40470 POS LEBIR (ARING) KELANTAN 102.3833 4.9333 91 0.40 3.68 126 40501 POS BELATIM KELANTAN 101.5333 5.0000 488 0.39 3.62 127 University40502 POS BIHAI (CHABAI) KELANTAN 101.5667 5.0000 275 0.42 3.84 128 40510 POS TEHOI (YAI) KELANTAN 101.7500 5.0500 198 0.42 3.87 129 40512 POS WIAS (KUALA WOOD) KELANTAN 101.8167 5.1167 76 0.44 4.04 130 40516 POS GOB KELANTAN 101.6333 5.2833 457 0.39 3.57 131 40517 MASJID BESAR JELI KELANTAN 101.8500 5.6833 93 0.42 3.83 132 40505 FELDA SUNGAI CIKU 3 KELANTAN 102.2167 5.0667 86 0.41 3.76 P. TER. HAIWAN TANAH 0.42 133 40546 MERAH KELANTAN 102.0092 5.8111 0 3.88 134 40547 MARDI JERAM PASU KELANTAN 102.3444 5.8128 31 0.40 3.64 PEJ.HAI. JAJAHAN 0.41 135 40586 MACHANG KELANTAN 102.2000 5.7667 31 3.72 136 40587 P. PERT. BATANG MERBAU KELANTAN 102.0500 5.8167 21 0.42 3.81

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137 40588 PUSAT PERTANIAN MELOR KELANTAN 102.3000 5.9667 7 0.40 3.66 PUSAT PERTANIAN 0.40 138 40660 BACHOK KELANTAN 102.4000 6.0500 3 3.61 139 40663 PUSAT. PERT. PASIR MAS KELANTAN 102.1167 6.0333 9 0.41 3.73 140 40664 PUSAT PERT. LUNDANG KELANTAN 102.2658 6.1044 6 0.40 3.65 141 40666 MARDI KUBANG KERANJI KELANTAN 102.2808 6.0833 8 0.40 3.65 142 49411 BALAI PENGHULU KERTIH TERENGGANU 103.4333 4.5167 0 0.36 3.39 143 49413 PENGKALAN GAWI TERENGGANU 102.6667 4.6667 159 0.38 3.53 144 49465 HOSPITAL KEMAMAN TERENGGANU 103.4167 4.2333 3 0.37 3.41 145 49476 HOSPITAL DUNGUN TERENGGANU 103.4167 4.7667 3 0.36 3.38 146 49410 FELDA NERAM SATU TERENGGANU 103.2833 4.0167 0 0.37 3.46 147 49454 FELDA JERANGAU TERENGGANU 103.1833 4.9000 0 0.37 3.42 148 49460 LADANG RAKYAT TERENGGANU 103.2500 4.1333 0 0.37 3.46 149 49461 FELDA CERUL 03 TERENGGANU 103.2500 4.1833 0 0.37 3.46 150 49466 MARDI KEMAMAN TERENGGANU 103.3167 4.2500 50 0.37 3.42 LADANG KOKO LANDAS 0.37 151 49480 JERANGAU TERENGGANU 103.1833 4.9333 0 3.42 152 49482 MARDI JERANGAU TERENGGANU 103.1500 4.9833 15 0.37 3.42 153 49503 PERTANIAN PADANG IPOH TERENGGANU 103.0167 5.0500 0 0.37 3.46 154 49508 FELDA BUKIT BADING TERENGGANU 103.1667 5.0167 43 0.37 3.41 155 49512 HAIWAN TERSAT TERENGGANU 103.0500 5.0500 0 0.37 3.45 156 49522 FELDA BELERA TERENGGANU 102.9500 5.3333 41 0.37 3.45 INSTITUT PERTANIAN 0.39 157 49539 BESUT TERENGGANU 102.6167 5.6500 10 3.56 Malaya0.38 158 42249 JHEOA KUALA ROMPIN PAHANG 103.5000 2.8167 0 3.64 159 42251 POS ISKANDAR PAHANG 102.6500 3.0333 0 0.40 3.79 160 42253 RPS KEDAIK PAHANG 103.2167 2.8833 0 0.39 3.69 PUSAT LATIHAN JHEOA Km of 0.38 161 42304 56 (Bt. 34) PAHANG 102.9167 3.6000 0 3.62 PERBADANAN KEMAJUAN 0.36 162 42318 BKT. FRASER PAHANG 101.7333 3.7167 1280 3.40 163 42320 HOSPITAL BENTONG PAHANG 101.9167 3.5167 97 0.40 3.80 TALIKOM BUKIT PENINJAU 0.36 164 42321 (FRASER HILL) PAHANG 101.7500 3.7167 1324 3.39 165 42322 POS BUKIT ROK PAHANG 102.5667 3.3667 0 0.40 3.74 166 42324 RPS RUNCANG PAHANG 103.1667 3.2833 15 0.38 3.59 167 42325 RPS BKT. SEROK ROMPIN PAHANG 102.8333 2.9167 40 0.39 3.74 168 42326 RPS BETAU LIPIS PAHANG 101.7167 4.2167 120 0.43 4.02 169 42327 POS SUNGAI DUA PAHANG 102.0000 3.4500 0 0.41 3.87 170 42332 P PERIKANAN BKT. TINGGI PAHANG 101.8167 3.3667 153 0.40 3.76 TAPAK SEMAIAN LENTANG, BENTONG, 0.40 171 42337 PAHANG PAHANG 101.8867 3.3900 153 3.75 172 University42344 POS PENDERAS PAHANG 102.3000 3.7333 0 0.41 3.84 173 42378 SEK. MEN. AHMAD PEKAN PAHANG 103.3333 3.4833 4 0.37 3.53 174 42402 JHEOA KUALA LIPIS PAHANG 102.0167 4.0333 122 0.41 3.86 175 42404 POS SENDERUT PAHANG 101.5833 4.1833 457 0.39 3.65 176 42420 POS TERISU PAHANG 101.4667 4.5333 0 0.47 4.42 177 42422 POS TELANOK PAHANG 101.5833 4.4333 351 0.41 3.79 178 42424 POS LANAI PAHANG 101.7333 4.3833 122 0.43 4.05 PST. PERTANIAN 0.38 179 42248 HORTIKULTOR JANGLAU PAHANG 103.5833 2.7167 0 3.64 180 42256 FELDA TEMBANGAU 6 PAHANG 102.6500 2.9333 0 0.40 3.82 181 42302 FELCRA PAYA LAMAN PAHANG 102.2333 3.4833 0 0.41 3.83

193

182 42307 FELCRA BOHOR BARU PAHANG 102.5833 3.3833 0 0.40 3.74 183 42308 FELDA MENGKARAK 1 PAHANG 102.2992 3.2869 0 0.40 3.82 184 42323 FELDA CHINI DUA PAHANG 103.0333 3.3833 17 0.38 3.60 185 42328 MARDI BUKIT RIDAN PAHANG 103.1047 3.0383 26 0.38 3.65 186 42329 FELDA MERCONG DUA PAHANG 103.1667 3.1000 0 0.39 3.65 187 42330 FELDA KAMPUNG SERTIK PAHANG 102.0333 3.5000 70 0.40 3.81 188 42333 FELDA KRAU 4 PAHANG 101.9833 3.6167 93 0.40 3.81 189 42334 FELDA BUKIT TAJAU PAHANG 102.7333 3.5833 43 0.39 3.64 190 42335 PUSAT PERT. GALI RAUB PAHANG 101.8500 3.7833 159 0.41 3.80 191 42336 FELDA KAMPONG AWAH PAHANG 102.5000 3.5833 40 0.39 3.72 PUSAT PERTANIAN JAMBU 0.41 192 42338 RIAS PAHANG 102.1500 3.4667 0 3.84 FELDA KAMPONG NEW 0.39 193 42339 ZEALAND PAHANG 102.8528 3.6453 0 3.64 FELDA JENDERAK 0.41 194 42340 SELATAN PAHANG 102.3000 3.6833 0 3.83 195 42341 FELDA JENDERAK UTARA PAHANG 102.3119 3.6786 46 0.40 3.78 196 42342 LADANG LEEPANG PAHANG 103.0272 3.0042 26 0.39 3.68 197 42343 FELDA LURAH BILUT PAHANG 101.8833 3.6667 0 0.42 3.94 198 42345 FELDA ULU JEMPOL PAHANG 102.6333 3.7500 0 0.40 3.71 FELDA SUNGAI PANCING 0.37 199 42349 SELATAN PAHANG 103.1667 3.8000 50 3.49 200 42351 FELDA JENGKA 14 PAHANG 102.5500 3.7333 50 0.39 3.69 201 42354 FELDA BUKIT KUANTAN PAHANG 103.1500 3.9167 0 0.37 3.51 P.P.TANAMAN KAMPUNG 0.40 202 42360 AWAH PAHANG 102.5000Malaya 3.5833 31 3.73 203 42363 FELDA PPP TUN RAZAK PAHANG 102.5667 3.8333 76 0.39 3.67 204 42365 MAKMUR DAIRY PAHANG 102.5347 3.8881 176 0.38 3.60 PUSAT PERTANIAN BUKIT 0.38 206 42381 GOH PAHANGof 103.0833 3.8333 31 3.52 207 42400 FELDA TERSANG SATU PAHANG 101.7833 4.0000 130 0.42 3.92 208 42403 FELDA PADANG PIOL PAHANG 102.3833 4.0333 0 0.41 3.84 LADANG FELDA KECHAU 0.43 209 42415 06 PAHANG 102.1667 4.2500 0 3.99 FELDA SUNGAI KOYAN 0.44 210 42416 SATU PAHANG 101.8667 4.2667 0 4.15 MARDI CAMERON 0.37 211 42421 HIGHLANDS PAHANG 101.3872 4.4681 1448 3.41 212 42464 MARDI SUNGAI BAGING PAHANG 103.3833 4.0667 4 0.37 3.44 213 47115 HOSPITAL PONTIAN JOHORE 103.3833 1.4833 5 0.40 3.87 214 47117 HOSPITAL JOHOR BARU JOHORE 103.7500 1.4667 15 0.38 3.72 215 47120 HOSPITAL KOTA TINGGI JOHORE 103.9000 1.7333 9 0.38 3.63 216 47121 JKR BANDAR KOTA TINGGI JOHORE 103.9000 1.7333 33 0.38 3.62 RUMAH API BKT. 0.42 217 47139 SEGENTING JOHORE 102.8833 1.8000 77 4.03 218 University47159 SINDORA JOHORE 103.4711 1.9636 81 0.38 3.70 219 47202 PUSAT BUKIT PASIR MUAR JOHORE 102.6333 2.1000 11 0.43 4.12 KLINIK KESIHATAN 0.38 220 47203 KAHANG JOHORE 103.5333 2.2167 53 3.66 221 47205 NUSAJAYA JOHORE 103.6167 1.4167 22 0.39 3.77 222 47206 HOSPITAL TANGKAK JOHORE 103.5333 2.2667 31 0.38 3.68 PEJ. KEJORA BANDAR 0.37 223 47207 PENAWAR JOHORE 104.2333 1.5500 35 3.55 224 47214 MAJLIS DAERAH LABIS JOHORE 103.0333 2.3833 40 0.40 3.82 MARDI ALOR BUKIT 0.40 225 47116 PONTIAN JOHORE 103.4500 1.5000 3 3.83 226 47118 PUSAT PERT.KONG KONG JOHORE 103.8833 1.4833 38 0.38 3.66 227 47122 FELDA SG. SIBOL JOHORE 103.6333 1.8333 61 0.38 3.67

194

228 47125 FELDA SG. MAS JOHORE 104.1500 1.6167 8 0.37 3.57 229 47127 FELDA BKT. WAHA JOHORE 104.1500 1.7500 40 0.37 3.55 230 47130 PUSAT PERT. PARIT BOTAK JOHORE 103.0833 1.7167 2 0.41 3.96 231 47134 C. RES. LAYANG LAYANG JOHORE 103.4678 1.8406 31 0.39 3.75 47137 KULIM MONTEL FARM 0.38 232 (47136) (LADANG BASIR ISMAIL) JOHORE 103.9167 1.6333 39 3.62 233 47140 P. PERT. SG. SUDAH JOHORE 102.7167 1.9000 2 0.43 4.13 234 47142 FELDA BUKIT BATU JOHORE 103.4333 1.7000 0 0.39 3.80 235 47144 FELDA TAIB ANDAK JOHORE 103.6447 1.7308 61 0.38 3.68 236 47146 FELDA BUKIT PERMAI JOHORE 103.6833 1.7667 49 0.38 3.67 237 47147 LADANG SG. AMBAT JOHORE 103.8667 2.1667 55 0.37 3.58 238 47157 FELDA AYER HITAM JOHORE 103.2333 1.9500 40 0.40 3.82 239 47158 MARDI KLUANG JOHORE 103.3667 1.9517 107 0.39 3.73 240 47200 HAIWAN KLUANG JOHORE 103.2100 1.9750 0 0.40 3.85 241 47201 CRAIGIELEA ESTATE JOHORE 102.7081 2.0756 41 0.42 4.07 FELDA TENGGAROH 0.38 242 47209 TIMUR 2 JOHORE 103.8667 2.0833 25 3.60 243 47211 CEP NIYOR ESTATE JOHORE 103.2500 2.0833 0 0.40 3.82 244 47212 LADANG BKT. PALOH JOHORE 103.2000 2.1333 39 0.40 3.81 245 47213 PUSAT PERT. PAGOH JOHORE 102.8500 2.0833 43 0.42 3.99 246 47215 FELDA LENGA JOHORE 102.8500 2.2500 36 0.41 3.95 FELDA BUKIT 0.41 247 47220 SERAMPANG JOHORE 102.8000 2.3333 0 3.97 248 47225 FELCRA PAYA KEPAR JOHORE 102.8667 2.3833 0 0.41 3.92 Malaya0.38 249 47229 FELCRA TANAH ABANG JOHORE 103.6489 2.4753 0 3.65 250 47232 FELDA MEDOI JOHORE 102.8833 2.5333 46 0.40 3.84 251 47236 GOMALI ESTATE JOHORE 102.6667 2.6167 0 0.41 3.93 of 0.38 252 47238 PERTANIAN ENDAU JOHORE 103.6167 2.6500 4 3.64

University

195

Appendix B: Solar Radiation Mapping

Malaya of

University

Figure B1 Solar Radiation Mapping over Peninsular of Malaysia for January.

196

Malaya of

University

Figure B2 Solar Radiation Mapping over Peninsular of Malaysia for February.

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Malaya of

University

Figure B3 Solar Radiation Mapping over Peninsular of Malaysia for March.

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Malaya of

University

Figure B4 Solar Radiation Mapping over Peninsular of Malaysia for April.

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Malaya of

University

Figure B5 Solar Radiation Mapping over Peninsular of Malaysia for May.

200

Malaya of

University

Figure B6 Solar Radiation Mapping over Peninsular of Malaysia for June.

201

Malaya of

University

Figure B7 Solar Radiation Mapping over Peninsular of Malaysia for July.

202

Malaya of

University

Figure B8 Solar Radiation Mapping over Peninsular of Malaysia for August.

203

Malaya of

University

Figure B9 Solar Radiation Mapping over Peninsular of Malaysia for September.

204

Malaya of

University

Figure B10 Solar Radiation Mapping over Peninsular of Malaysia for October.

205

Malaya of

University

Figure B11 Solar Radiation Mapping over Peninsular of Malaysia for November.

206

Malaya of

University

Figure B12 Solar Radiation Mapping over Peninsular of Malaysia for December.

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Appendix C: Grid Connected System

Figure C1 Solar PV has been used in the present analysis in UMPEDAC building Malaya of

University Figure C2 The controlling has been used in the present analysis in UMPEDAC building

208

Figure C3 Grid connected components has been used, the picture show: (inverter, meter, and connecting switch). Malaya of

Figure C4 The direct connected PC show the fluctuation in the DC voltage and current of UniversityPV.

209

Figure C5 Underneath PV array data logger has sensor recording weather temperature and humidity.

Malaya

Figure C6 Wireless weather station from ofDavis Company has been used to transferee the weather parameter.

University

Figure C7 List of Temperature sensors and Pyrometers has been used in the present analysis

210

Appendix D: Questionnaire and Survey Data

Part A: Questionnaire questions

Solar energy applications in Malaysia under feed-in-tariff program

Respondent’s Background:

Area of Domicile

Urban/Sub-urban/Rural

Name: Education: City:

Age:

□ 15-25 □ 26-40 □ 41-60 Malaya

Question 1. Are you concerned about environmental issues? of □ Yes □ No □ No opinion

Question 2. Do you know about “global Warming” / “Climate Change” and what mean for coastal places' country like Malaysia? Have you heard of global warming / climate change?

□ Yes, I hear but don’t know what it is mean for Malaysia. □ Yes, I hear but and know what it is mean for Malaysia. □University No, I did not hear about it.

Question 3. Do you like renewable energy? If yes, what type of renewable energy you like?

□ Solar Energy □ Wind Energy □ Nuclear Energy □ Geothermal Energy □ Tidal □ Don’t Know

211

Question 4. Which type of renewable energy feels suitable for Malaysia?

□ Solar Energy □ Wind Energy □ Nuclear Energy □ Geothermal Energy □ Tidal

Question 5. Do you think that solar radiation in Malaysia is a promising for the applications of solar energy?

□ Yes

□ No

□ No opinion

Question 6. Are you interest in solar PV applications?

□ Yes

□ No

□ No opinion

Malaya

Question 7. Which of the following would you consider or have implemented in your own home? (Mark all that apply). of

Photovoltaic-generated electricity Solar-heated hot water Solar-powered space cooler Solar-heated pool No any one

Question 8. If one (or more) solar energy project was carried out in your region, would it contribute to solar energy awareness?” □ Agree □ Disagree □ No opinion University

Question 9. Would you like to start photovoltaic-generated electricity in your home?

□ Yes, but did not have enough money. □ Yes, but did not have enough space. □ Yes, but did not have both money and space. □ No, don’t like

212

Question 10. Did you hear about Feed -in-Tariff, and what it is mean?

□ Yes, I hear but don’t know what it is mean. □ Yes, I hear but and know what it is mean. □ No, I did not hear about it.

? Question 11. IF you hear about Feed-in-Tariff, do you think that the government incentives enough to start photovoltaic-generated electricity in your home town?

□ Yes □ No □ No opinion

Question 12. Do you know how much the capital cost of 1 kWp of grid-connected photovoltaic-generated electricity is?

□ Yes, it is approximately; □ less than 7500RM □ between 7500-12500 □more □ No □ No opinion Malaya

Question 13. Do you know how many years the payback period of invested money in gird photovoltaic-generated electricityof under the Feed-in-Tariff in your home town is?

□ Yes, it is approximately;□ less than 6 □ between 6-10 □more □ No □ No opinion / Unsure

Question 14. If any Bank offer to share you the benefit of selling electricity to the government under Feed -in-Tariff and give you a loan to start grid connected photovoltaic-generated electricity project, and you first have to covered the loan from sell the electricity during a period between 5-8 years, will you accept this University offer?

□ Agree □ Disagree □ No opinion

213

Part B: Questionnaire feedback Tables show the people’s feedbacks utilize solar-energy applications in Malaysia under the feed-in-tariff program.

Table D1 Results for survey question 1.

□ Yes □ No □ No opinion □ No answer Total

Frequency 537 23 16 4 580 Percent 93% 4% 3% 1% 100%

Table D2 Results for survey question 2:

□ Yes □ No □ No opinion □ No answer Total

Frequency 550 30 0 0 580 Percent 95% 5% 3% 0% 100%

Table D3 Results for survey question 3.

Frequency Percent □Solar Energy 472 Malaya81% □Wind Energy 60 10% □Nuclear Energy 24 4% □Geothermal Energy of8 1% □Tidal 13 2% □Don’t like 0 0% □No opinion 3 1% Total 580 100%

Table D4 Results for survey question 4.

Frequency Percent □Solar Energy 487 84% □Wind Energy 36 6% □Nuclear Energy 25 4% □Geothermal Energy 13 2% University□Tidal 13 2% □No opinion 6 1% Total 580 100%

Table D5 Results for survey question 5.

□ Yes □ No □ No opinion □ No answer Total

Frequency 366 31 177 6 580 Percent 63% 5% 31% 1% 100%

214

Table D6 Results for survey question 6.

□ Yes □ No □ No opinion □ No answer Total

Frequency 297 82 190 11 580 Percent 51% 14% 33% 2% 100%

Table D7 Results for survey question 7.

Frequency Percent □ Photovoltaic-generated electricity 204 35% □ Solar-heated hot water 158 27% □ Solar-powered space cooler 40 7% □ Solar-heated pool 11 2% □ No any one 168 29% Total 580 100%

Table D8 Results for survey question 8.

□ Agree □ Disagree □ No opinion □ No answer Total

Frequency 411 27 137 5 580 Percent 71% 5% 24% 1% 100% Malaya Table D9 Results for survey question 9. of Frequency Percent □ Yes, but did not have enough money. 210 36% □ Yes, but did not have enough space. 66 11% □ Yes, but did not have both money and space. 215 37% □ No, don’t like 89 15% □ No answer 9 2% Total 580 100%

Table D10 Results for survey question 10.

Frequency Percent □Yes, but don’t know what it is mean 87 15% University□Yes, and know what it is mean 87 15% □No, I did not hear about it 405 70% □No answer 1 0% Total 580 100%

215

Table D11 Results for survey question 11.

□ Yes □ No □ No opinion □ No answer Total

Frequency 53 117 382 28 580 Percent 19% 20% 66% 5% 100%

Table D12 Results for survey question 12.

□ Yes □ No □ Unsure □ No answer Total

Frequency 52 0 511 17 580 Percent 9% 0% 88% 3% 100%

Table D13 Results for survey question 13.

□ Yes □ No □ Unsure □ No answer Total

Frequency 70 0 501 9 580 Percent 12% 0% 86% 2% 100%

Table D14 Results for survey question 14.

□ Agree □ Disagree □ No opinion □ No answer Total Malaya Frequency 242 90 242 6 580 Percent 42% 16% 42% 1% 100% of

University

216

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