Accuracy Improvement of Predictive Neural Networks for Managing Energy in Solar Powered Wireless Sensor Nodes

DISSERTATION

zur Erlangung des akademischen grades Doktor – Ingenieur (Dr.-Ing)

vorgelegt der Fakultät für Elektrotechnik und Informationstechnik der Technischen Universität Chemnitz

von M.Sc. Murad AL_Omary geboren am 09.08.1987 in As Sarih, Jordanien

Tag der Einreichung : 14. Oktober 2019 Tag der Verteidigung : 1 6. Dezember 2019

Gutachter : Prof. Dr.-Ing. Olfa Kanoun Prof. Dr.-Ing. Nabil Derbel

Acknowledgement

It is a great feeling, after finishing the Ph.D. to look backward to all stations I have been through. All the memories passes in front of my eyes as a movie. Oh I’m free now, I have finished my Ph.D. lastly. However, this work was impossible to be done without the support of many people, whom I need to thanks sincerely and from my heart. Above all, I would like to thank God for my success with this chapter of my life.

I express my deep sense of gratitude and thanking to Prof. Dr.-Ing. Olfa Kanoun for supervising me all the time, for the extremely motivational conversations as well as the valuable directions and advices. Working with you was and will remain an honor and pride for me all my life.

Also, I thank all my colleagues from the MST institute for the unlimited cooperation during the working hours. In particular, the members of energy harvesting group, each according to his own name for granting me a part of their experiences and for the precious feedbacks during our meetings. All the students who worked with me continuously and hardly deserve a thanking words too.

I would like to thank German Jordanian University, not only for their partial financial support during my Ph.D. study. But also for giving me the opportunity to be one of its members after obtaining the title (Dr.-Ing). I don’t forget also my lovely university, TU Chemnitz for funding me as a teaching and research assistant and for the beautiful moments I lived in. The financial support from the German Academic Exchange Service (DAAD) through “InProTUC”, “PROMOS” and “STIBET” scholarships are gratefully acknowledged.

Last, and certainly not least, I am vastly indebted to my wonderful wife “Eman”. I would like to thank her for understanding me during my Ph.D. work. I would like to thank my mother “Moyasser”, whose love and prayers always strengthen me and push me forward. Many thanks for my brothers, “Yazan” and “Malek”, for their continuous encouragement especially at the difficult times.

Murad AL_Omary Chemnitz, Oct. 2019

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Dedication

To the spirit of my late father,

“Abdullah”

To my lovely twins,

“Reman” & “Lilian”

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Abstract

Wireless Sensor Network (WSN) is a technology that measures an environmental or physical parameters in order to use them by decision makers with a possibility of remote monitoring. Normally, sensor nodes that compose these networks are powered by batteries which are no longer feasible, especially when they used as fixed and standalone power source. This is due to the costly replacement and maintenance. Ambient energy harvesting systems can be used with these nodes to support the batteries and to prolong the lifetime of these networks.

Due to the high power density of solar energy in comparison with different environmental energies, solar cells are the most utilized harvesting systems. Although that, the fluctuating and intermittent nature of solar energy causes a real challenge against fulfilling a functional and reliable sensor node.

In order to operate the sensor node effectively, its energy consumption should be well managed. One interesting approach for this purpose is to control the future node’s activities according to the prospective energy available. This requires performing a prior prediction of the harvestable solar energy for the upcoming operation periods including the sun’s free times. A few prediction algorithms have been created using stochastic and statistical principles as well as artificial intelligence (AI) methods. A considerable prediction error of 5-70% is realized by these algorithms affecting the reliable operation of the nodes. For example, the stochastic ones use a discrete energy states which are mostly do not fit the actual readings. The statistical methods use a weighting factors for the previous registered readings. Thus, they are convenient only to predict energy profiles under consistent weather conditions. AI methods require large observations to be used in the training process which increase the memory space needed. Accordingly, the performance concerning the prediction accuracy of these algorithms is not sufficient.

In this thesis, a prediction algorithm using a neural network has been proposed and implemented in a microcontroller for managing energy consumption of solar cell driven sensor nodes. The utilized neural network has been developed using a combination of meteorological and statistical input parameters. This is to meet a required design criteria for the sensor nodes and to fulfill a performance exceeds in its accuracy the performance of aforementioned traditional algorithms. The prediction accuracy represented by the correlation coefficient has been registered for the developed neural network to be 0.992, which increases the most accurate traditional network which has a value 0.963.

VII

The developed neural network has been embedded into a sensor node prototype to adjust the operating states or modes over a simulation period of one week. During this period, the sensor node has worked 6 hours more towards normal operation mode. This in its role helped to fulfill an effective use of available energy approximately 3.6% better than the most accurate traditional network. Thus, longer lifetime and more reliable sensor node.

Keywords: Wireless Sensor Network (WSN), Energy Harvesting, Energy Management, Artificial Neural Network (ANN), Prediction Algorithms, Global Solar Radiation (퐺푆푅).

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Kurzfassung

Das drahtlose Sensornetzwerk (WSN) ist eine Technologie, die Umgebungsbedingungen oder physikalische Parameter misst, weiterleitet und per Fernüberwachung zur Verfügung stellt. Normalerweise werden die Sensorknoten, die diese Netzwerke bilden, von Batterien gespeist. Diese sollen aus verschiedenen Gründen nicht mehr verwendet werden, sondern es wird auf eine eigenständige Stromversorgung gesetzt. Dies soll den aufwendigen Austausch und die Wartung minimieren. Energy Harvesting kann mit den Knoten verwendet werden, um die Batterien zu unterstützen und die Lebensdauer der Netzwerke zu verlängern.

Aufgrund der hohen Leistungsdichte der Solarenergie im Vergleich zu verschiedenen anderen Umweltenergien sind Solarzellen die am häufigsten eingesetzten Wandler, allerdings stellt die schwankende und intermittierende Natur der Solarenergie eine Herausforderung dar, einen funktionalen und zuverlässigen Sensorknoten zu versorgen.

Um den Sensorknoten effektiv zu betreiben, sollte sein Energieverbrauch sinnvoll gesteuert werden. Ein interessanter Ansatz zu diesem Zweck ist die Steuerung der Aktivitäten des Knotens in Abhängigkeit von der zukünftig verfügbaren Energie. Dies erfordert eine Vorhersage der wandelbaren Sonnenenergie für die kommenden Betriebszeiten einschließlich der freien Zeiten der Sonne. Einige Vorhersagealgorithmen wurden mit stochastischen und statistischen Prinzipien sowie mit Methoden der künstlichen Intelligenz (KI) erstellt. Durch diese Algorithmen bleibt ein erheblicher Vorhersagefehler von 5-70%, der den zuverlässigen Betrieb der Knoten beeinträchtigt. Beispielsweise verwenden die stochastischen Methoden einen diskreten Energiezustand, der meist nicht zu den tatsächlichen Messwerten passt. Die statistischen Methoden verwenden einen Gewichtungsfaktor für die zuvor registrierten Messwerte. Daher sind sie nur geeignet, um Energieprofile bei konstanten Wetterbedingungen vorherzusagen. KI-Methoden erfordern große Beobachtungen im Trainingsprozess, die den benötigten Speicherplatz erhöhen. Dementsprechend ist die Leistung hinsichtlich der Vorhersagegenauigkeit dieser Algorithmen nicht ausreichend.

In dieser Arbeit wird ein Vorhersagealgorithmus mit einem neuronalen Netzwerk entwickelt und eingebunden in einen Mikrocontroller, um die Verwaltung des Energieverbrauchs von solarzellengesteuerten Sensorknoten zu optimieren. Das verwendete neuronale Netzwerk wurde mit einer Kombination aus meteorologischen und statistischen Eingangsparametern realisiert. Dies hat zum Ziel, die erforderlichen Designkriterien für Sensorknoten zu erfüllen und eine Leistung zu erreichen, die in IX

ihrer Genauigkeit die Leistung der oben genannten traditionellen Algorithmen übersteigt. Die Vorhersagegenauigkeit die durch den Korrelationskoeffizienten repräsentiert wird, wurde für das entwickelte neuronale Netzwerk auf 0,992 bestimmt. Das genaueste traditionelle Netzwerk erreicht nur einen Wert von 0,963.

Das entwickelte neuronale Netzwerk wurde in einen Prototyp eines Sensorknotens integriert, um die Betriebszustände oder -modi über einen Simulationszeitraum von einer Woche anzupassen. Während dieser Zeit hat der Sensorknoten 6 Stunden zusätzlich im Normalbetrieb gearbeitet. Dies trug dazu bei, eine effektive Nutzung der verfügbaren Energie um ca. 3,6% besser zu erfüllen als das genaueste traditionelle Netz. Dadurch wird eine längere Lebensdauer und Zuverlässigkeit des Sensorknotens erreicht.

Schlagwörter: Drahtlose Sensornetzwerke (Wireless Sensor Network, WSN), Energiegewinnung, Energiemanagement, Künstliche Neuronale Netz (Artificial Neural Network, ANN), Vorhersagealgorithmen, Globale Sonnenstrahlung, (Global Solar Radiation, 퐺푆푅).

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Table of Contents Acknowledgement ...... III Dedication ...... V Abstract ...... VII Kurzfassung ...... IX Abbreviations ...... XV Symbols ...... XVI Physical Constants ...... XX 1 Introduction ...... 1 1.1 Motivation ...... 1 1.2 Problem statement ...... 3 1.3 Thesis objectives ...... 3 1.4 Thesis overview ...... 4 2 Theoretical background ...... 7 2.1 Wireless sensor network ...... 7 2.2 Sensor node structure ...... 9 2.2.1 Energy harvesting unit...... 10 2.2.2 Wireless sensor unit ...... 10 2.3 Energy management of WSN ...... 10 2.3.1 Definition of energy management ...... 11 2.3.2 Classifications of energy management ...... 11 2.3.3 Levels of energy management...... 12 2.3.4 Components of energy management ...... 14 2.3.5 Principles of energy management ...... 15 2.4 Solar powered sensor nodes ...... 16 2.5 Geometry of solar energy ...... 17 2.5.1 Types of solar radiation ...... 17 2.5.2 Solar angles ...... 19 2.5.2.1 Declination angle (훿) ...... 19 2.5.2.2 Zenith angle (휃푧)...... 20 2.5.2.3 Azimuth angle (휓) ...... 21 2.5.2.4 Time angle (휔) ...... 21 2.5.3 Influencers of solar energy ...... 22 2.5.3.1 Atmospheric attenuators ...... 22 2.5.3.2 Air mass effect ...... 24 2.5.3.3 Multiple reflections effect ...... 25 2.5.3.4 Tilted surface effect ...... 27 2.5.4 Solar cells ...... 28

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2.5.4.1 Electrical equivalent circuit of solar cell ...... 29 2.5.4.2 Performance of solar cell ...... 30 3 State of the art for solar energy prediction in wireless sensor nodes ...... 33 3.1 Classification of solar energy prediction algorithms ...... 33 3.2 History of solar energy prediction algorithms ...... 34 3.3 Stochastic prediction algorithms ...... 36 3.4 Statistical prediction algorithms ...... 37 3.4.1 Exponentially weighted algorithms ...... 37 3.4.1.1 EWMA ...... 37 3.4.1.2 ASEA ...... 38 3.4.1.3 SEP-DR ...... 39 3.4.1.4 SEPAD ...... 40 3.4.2 Weather conditioned algorithms...... 41 3.4.2.1 WCMA ...... 41 3.4.2.2 WCMA-PDR ...... 43 3.4.2.3 WCSMA ...... 44 3.4.2.4 AR-WCMA ...... 46 3.4.2.5 D-WCMA ...... 47 3.4.2.6 UD-WCMA ...... 48 3.4.3 Profile energy algorithms ...... 49 3.4.3.1 Pro-Energy ...... 49 3.4.3.2 IPro-Energy ...... 50 3.4.3.3 Pro-Energy-VLT ...... 51 3.5 Artificial Intelligence (AI) prediction algorithms ...... 52 3.5.1 Fuzzy Logic ...... 52 3.5.2 Neural Networks ...... 53 3.6 Performance comparison of prediction algorithms ...... 55 3.7 Adopted methodology ...... 57 4 Accuracy improvement of an adapted predictive neural network ...... 59 4.1 Challenges of predictive neural networks in wireless sensor nodes ...... 59 4.2 Objectives of predictive neural networks in wireless sensor nodes ...... 61 4.3 Neural networks using meteorological input parameters ...... 62 4.4 Adaptation of predictive neural network using meteorological input parameters for wireless sensor nodes ...... 63 4.4.1 Identifying of suitable meteorological input parameters...... 63 4.4.2 Identifying the suitable topology ...... 65 4.5 Data base for input and output parameters ...... 65 4.6 Layout for modeling zenith angle as input parameter ...... 66

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4.7 Modeling of zenith angle ...... 67 4.8 Evaluation of prediction accuracy ...... 70 4.8.1 Accuracy of adapted neural network ...... 70 4.8.2 Accuracy of main statistical algorithms ...... 72 4.8.3 Accuracy of traditional neural networks ...... 76 4.9 Accuracy improvement of adapted neural network ...... 80 4.9.1 Selection of the suitable improving statistical algorithm ...... 81 4.9.2 Evaluation the accuracy of improved neural network ...... 82 4.9.3 Weights and biases of improved neural network ...... 84 5 Implementing of the proposed predictive neural network in sensor nodes .... 87 5.1 Definition of operating states ...... 87 5.2 Operating conditions ...... 89 5.3 Implementation on hardware ...... 91 5.4 Prediction errors ...... 93 5.5 Distribution of operating states ...... 96 6 Conclusions and future works ...... 101 6.1 Conclusions ...... 101 6.2 Future works ...... 103 References ...... 105 Thesen ...... 118 List of Figures ...... 121 List of Tables ...... 125 Appendices ...... 127 Declaration of Authorship...... 132 Erklärung zur Urheberschaft ...... 132 CV of Author ...... 134 List of Publications ...... 138

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Abbreviations

Abbreviations

AI Artificial Intelligence ANN Artificial Neural Network AP Atmospheric Pressure at solar cell location AR-WCMA Autoregressive Weather Conditioned Moving Average ASIM Accurate Solar Irradiance Model ASEA Accurate Solar Energy Allocation AT Air Temperature 퐵푅 Direct Radiation CC Cloud Cover 퐷푅 Diffuse Radiation ER Extraterrestrial Radiation EWMA Exponentially Weighted Moving Average 퐹퐹 Fill Factor IC Integrated Circuit IPro-Energy Improved Profile-Energy 퐺푆푅 Global Solar Radiation LASER Light Amplification by Simulated Emission Radiation 푀퐴퐸 Mean Absolute Error MSE Mean Square Error MPPT Maximum Power Point Tracking Pro-Energy Profile Energy method Pro-Energy-VLT Profile Energy at Variable Lengths Time RF Radio Frequency 푅푀푆퐸 Root Mean Square Error RH Relative Humidity SD Sunshine Duration SEPAD Solar Energy Prediction based on Additive Decomposition SEP-DR Solar Energy Prediction based on Daily Ratio D-WCMA Dynamic Weather Conditioned Moving Average UD-WCMA Universal-Dynamic Weather Conditioned Moving Average WS Wind Speed WSN Wireless Sensor Network WCMA Weather Conditioned Moving Average WCMA-PDR Weather Conditioned Moving Average-Phase Displacement Regulator WCSMA Weather Condition Selective Moving Average

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Symbols

Symbols

푎푖 Coefficients for AR-WCMA algorithm AM Air mass 퐴푑 Attenuation factor 퐴푃푠푡푎 Atmospheric pressure at standard condition 퐴푃푂 Atmospheric pressure at sea level 푏푗 Bias of neuron 푗 퐵푅휆 Direct radiation after reduction or attenuation 퐵푅푎푏푠 Absorbed direct radiation 퐵푅푡푖푙푡푒푑 Direct radiation at tilted cell 훽퐴 Extinction coefficient 푐 Constant value 퐶 Vector of current day energy reservations 푑 Current day 푑푖푠 Distance 푑푛 Duration of time slot 푛 푑표푟푑푒푟 Order of the day in the year. 퐷 Past days 퐷푟푎푡푖표 Daily ratio 퐷푅푡푖푙푡푒푑 Diffuse radiation at tilted cell 퐸푎푣푎 Total energy available for the sensor node 퐸퐵 Energy stored in the battery 퐸퐵0 Energy available in the battery at initial state 퐸퐵푚푎푥 Maximum energy capacity in the battery 퐸푐 Emission coefficient 퐸푐푝 Energy predicted according to diurnal cycle 퐸푠푝 Energy predicted according to seasonal cycle 퐸푐표푛 Energy consumed by sensor node 퐸푑푝 Energy predicted according to current daily trend 퐸푖푝 Initial energy prediction 퐸푖푛푖 Initial level of energy 퐸푚푖푛 Minimum level of energy 퐸푚푎푥 Maximum level of energy 퐸푛 Nominal level of energy 퐸푠푢푝 Energy supplied by solar cells 퐸푃퐷푅 Energy predicted in WCMA-PDR algorithm 휖 Ratio between the actual harvested and predicted energy 휀푎 Absorptivity of absorber (solar cell) 휀푔 Absorptivity of glass 퐺1, 퐺2 Gains 퐺퐴푃 Factor reflects the current day solar condition relative to the past days 퐺푆푅푡푖푙푡푒푑 Global solar radiation at tilted cell

XVI

Symbols

ℎ Elevation angle 퐻 Harvested energy 퐻̃ Harvested energy according to auto regression process 퐻푑 Most similar energy profile 푑 퐻푛+1 Extracted profile at the predictable time slot 푛 + 1 퐻푠푢푛푛푦 Matrix of harvested energy for sunny days 퐻푐푙표푢푑푦 Matrix of harvested energy for cloudy days 퐻푚푖푥 Matrix of harvested energy for days of mixed weather conditions 퐻푚 Selected matrix of harvested energy 퐼푃푉 Output current of solar cell 퐼푠ℎ Leakage current 퐼푆퐶 Short circuit current of solar cell 퐼푝ℎ Current source 퐼푠 Saturation current 퐼퐷 Diode current 퐼푀푃푃 Current at maximum power point 퐼푖푛푖 Current driven at initial level of energy 퐼푛 Current driven at nominal level of energy 퐼푚푖푛 Current driven at minimum level of energy 퐼푚푎푥 Current driven at maximum level of energy 퐽/푐푚2 Joule per square centimeter 퐽/퐾 Joule per Kelvin 푘 Size of vectors 푉 and 푃 퐾 Number of past samples considered for the calculation km Kilo meter 푚 Air mass 푚푊 Air mass of water steam 푚푆푃 Air mass of scattering particles 푀퐷 Mean value 푛 Time slot 푛 + 1 Predictable time slot 푛푚 Nano meter 푛푟푒푓 Times of reflections 푛푛푒푤 Sub-time slots in the time slot 푛 푁 Number of timeslots per a day. 푁퐷푎푦푙푖푔ℎ푡 Number of daylight hours 푝푘 Element in vector 푃 푃 Vector used to give an importance to the closest energy profiles 푃푀푃푃 Power at maximum power point 푃푛 Average power harvested at time slot 푛 푃퐸 Prediction error vector 푃푚푎푡 Probability matrix 푞 Electrical charge of the electron (= 1.6 × 10−19 Coulomb) 푟푘 Residual constant term XVII

Symbols

푅 Correlation coefficient 푅푠 Series resistor 푅푠ℎ Shunt resistor 푅2 Coefficient of determination 푅퐸푡푖푙푡푒푑 Reflected radiation at tilted cell 푆푗 Signal at neuron 푗 푆퐹 Scaling factor 푆퐼푀 Variable denotes to the highest similarity 푇 Total number of hourly samples of 퐺푆푅 푇푑 Diode temperature 푣푘 Element in vector 푉 푉푃푉 Output voltage of solar cell 푉퐷 Diode voltage 푉푡ℎ Thermal voltage 푉 Vector used to indicate ratios of harvested energy to mean energy 푉푂퐶 Open circuit voltage of solar cell 푉푀푃푃 Voltage at maximum power point 휔 Time angle 휔푠 Time angle at sunrise and sunset 휔푛 Weight of variable length time slots 푤푖푗 Weight of connection between neuron 푖 and neuron 푗 푊푠푡푒푎푚 Water steam content in the atmosphere 푊/푐푚2 Watt per square centimeter 푊/푚2 Watt per square meter 푊푃 Weighted profile 푊푝표푟 Factor indicate the width of the portion of 푃퐸 vector 푥푖 Weighting coefficient in WCSMA algorithm 푥푡 Measured 퐺푆푅 at specific time 푥푛+1 Energy state for the next time slot 푋푖 Input at neuron 푖 푦푡 Predicted 퐺푆푅 at specific time 푧 Number of the time slots merged together 휃푧 Zenith angle 휗 Weighting factor for WCMA-PDR algorithm 휂 Weighting factor for calculating the prediction error vector 푃퐸 훿 Declination angle 휑 Geographic latitude 휓 Azimuth angle ° Degree 휆 Wavelength 휏 Common transmission coefficient 휏퐺 Transmission coefficient for mixed gases 휏푆푃 Transmission coefficient for scattering particles 휏푊 Water transmission coefficient XVIII

Symbols

휎퐺 Absorption coefficient of mixed gases 휎푊 Absorption coefficient of steam 휎1, 휎2 Standard deviations 훼, 훽, 훾 Weighting factors 훼퐴 Particle size 휉 Tilt angle 휌퐺 Ground reflectivity (Albedo factor) 휌푎 Reflectivity of absorber 휌푔 Reflectivity of glass

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Physical Constants

Physical Constants

−23 Boltzmann’s constant (퐵푧) 1.38 × 10 J/K Solar constant 1367 푊/푚2

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Introduction 1

1 Introduction

The measurement of different physical and environmental parameters is now a key for many processes in several technical applications; especially the medical, agricultural, industrial, and martial ones [1–15]. Thus, making a decisions without measuring or even using sensing devices may leads to several accidents and a significant property losses. These devices not only facilitate the life activities, but also save money and reduce human efforts. However, their variety in addition to their falling prices due to the high production numbers make them reachable and widely used.

To increase the benefit from these sensors, combining them together within a network is a solution. This enables observing the measured values over larger area. It also achieves better statistical processing due to the number of the utilized sensors. Although of these advantages, accompanying obstacles have been arose and still needed to overcome like the need for further wireless communication among them from one side and with the base stations from another side.

The recent developments in the field of sensor technology do not only go towards producing smaller and more energy-efficient sensors, but also towards improving the performance of their computing systems in order to enhance their abilities for operating effectively. All these developments in addition to others allow more utilizations in more several applications.

1.1 Motivation

Wireless Sensor Network (WSN) is a leading technology in the world of energy, electrical and communication systems. This technology consists of a number of sensor nodes, connected wirelessly and deployed over a target area. These nodes are mainly utilized to collect a desired measurements in that area with the applicability of monitoring them remotely. These nodes are described with a small size and relative cheap cost. Each node, in the primarily form, includes a components of energy storage unit, sensing unit, wireless transceiver (communication unit), and microcontroller to control operations of the node. According to the variety of sensors, the networks can be used to monitor different phenomena [16].

Motivation 2

The most significant challenge that faces these networks is the short lifetime of the composed sensor nodes. The short lifetime, basically and negatively, affects the robustness and reliability level of the networks which is strongly required to be high level through providing a permanent and continuous service. However, the short lifetime is clearly due to using the batteries as power sources, where the energy stored is restricted with limited quantity. Although that these batteries are rechargeable, replacing them with new ones when they depleted is difficult to some extent. This is attributed to presence of the sensors in rugged and distant places as well as the financial burden of the maintenance visits. Large energy storage devices are impossible to use with these nodes, because of the high cost of transporting these devices as well as the design's restrictions for the system.

As an alternative solution to power these sensors, extracting an energy from the ambient or surroundings is trended. This seems a convincing solution that the nodes are replenished with energy in the field. Thus, the maintenance visits become limited. Additionally, this allows for smaller energy storage devices to be used, which in turn reduces deployment cost associated with transport of larger devices, as well as potentially avoiding technologies inappropriate for the given site [17,18]. However, the ambient energy is available in the environment on different forms like thermal, mechanical, solar, wind, and radio frequency energies. Consequently, different harvesting devices were built for this purpose considering the suitability to the source available [19,20]. From other side, the nature of the environmental energy is classified according to controllability and predictability. Table 1.1 shows different energy sources with its corresponding classification and harvesting devices [20,21]. Among these forms, solar energy is the most widespread with wireless sensor networks. This is attributed to its large power density (roughly, 15 푊/푐푚2), its applicability with outdoor deployments, as well as low cost and size of solar panels [20,22,23].

Table 1. 1. Energy sources with corresponding harvesting devices [20]

Energy source Source’s Nature Harvesting device Thermal Fully controllable Thermoelectric element Mechanical Uncontrollable and unpredictable Piezoelectric transducer Solar Uncontrollable and predictable Photovoltaic cells Radio frequency Partially controllable Antenna

Introduction 3

1.2 Problem statement

The fluctuating and intermittent nature of solar energy causes a new and real challenge against fulfilling a reliable sensor nodes. From one side, an optimal use of the energy harvested is needed and a continuous constant service of the system is required from other side. Thus, we are in the front of energy management problem for the overall system. Accordingly, different energy management schemes have been created, examined and applied on the wireless sensor nodes.

The most common management scheme is based on a prior knowledge of harvesting opportunities. Through this scheme, energy consumed by wireless sensors can be controlled according to the energy available. In this regard, different prediction methods of harvestable solar energy were created with different prediction errors accompanied. The more accurate prediction algorithm refers to longer lifetime of the nodes which is finally translated into more reliable systems and networks.

1.3 Thesis objectives

Deployment of a wireless sensor network in a remote locations is a costly project. The cost of deployment can be reduced by including energy harvesting technologies to the composed nodes. These technologies enable the nodes to replenish energy in the field and reduce the size of energy storage devices. However, energy consumption must be managed to fulfill an optimal use of the available environmental energy. The main objective of the work described in this thesis is to support the success of remotely deployed WSN through an effective energy management. This is accomplished by:

 Creating a method for predicting the availability of harvestable solar energy; the method must be simple to allow implementation on limited hardware of typical platform of the wireless sensor node. This task is accomplished using neural network predictive controller of improved prediction accuracy.

 Applying a suitable energy management strategy or methodology to maximize performance of wireless sensor node; considering the restrictions of hardware and uncertainty of environmental conditions. This task is performed by adopting a neutral balance between harvested and consumed energy.

 Validating the developed approach through simulating a wireless sensor node, using real environmental data and realistic model of a node hardware.

Thesis overview 4

The neural based prediction is intended to be a decision-making tool for the energy management strategy, and to allow intelligent distribution of incoming energy so that it can be used as ideally as possible, avoiding downtime or wasting excess energy. This increases the reliability of the entire system. Furthermore, the controller should enable a forecasting function, whereby the energy management is completed with a predictive option.

1.4 Thesis overview

This thesis is composed of six chapters as in (Fig. 1.1).

In Chapter 1, there is an introduction about the technology of “Wireless Sensor Network”. The problem accompanying with these networks is viewed in this chapter. In addition to this, a motivation for managing energy in the composed sensor nodes is presented here. The main objectives to be reached in this thesis are declared too.

Chapter 2 shows a theoretical background which mainly is divided into two parts. The first one handles the topic of wireless sensor networks, structure of the composed sensor nodes and the energy management for these nodes. In the second part, the geometry of solar energy including the types of solar radiation, solar angles, different factors affect the availability of solar energy on the Earth and solar cells are discussed.

In Chapter 3, the state of the art for different solar energy prediction algorithms in wireless sensor node is shown and explained in details. This includes the stochastic, statistical and Artificial Intelligence (AI) methods. The performance indices of all these methods are also discussed.

Chapter 4 is devoted to treat the prediction of solar energy using neural networks. This chapter highlights firstly the objectives, advantages and challenges of neural network. Then, a necessary network to be used with wireless sensor is created explaining the required topology and input parameters. Additionally, an improvement for the prediction accuracy of this network is performed in this chapter.

In Chapter 5, the diagnostics resulted from integrating and implementing the developed neural network, traditional neural networks as well as the main prediction algorithms into solar powered wireless sensor nodes have been shown and evaluated.

Introduction 5

Last and not least, Chapter 6 summaries the results of this work through a conclusion. Also, a future works in the field of solar powered sensor nodes have been suggested in this chapter.

Chapter 1 Introduction

Chapter 2 Theoretical background

State of the art for solar energy

Chapter 3 prediction in wireless sensor nodes

Accuracy improvement of adapted predictive neural network

Chapter 4

Analysis to calculate the prediction accuracy

Implementing of the proposed

Chapter 5 predictive neural network in sensor nodes

Conclusions and Chapter 6

future works

Fig.1. 1. Structure of thesis

Theoretical background 7

2 Theoretical background

This chapter shows firstly a theoretical background about wireless sensor network in Sec. 2.1. Then, a description for the structure of the composed sensor node in Sec. 2.2. This mainly deals with both, harvesting unit and wireless sensor unit. An explanation for the term of energy management in that nodes is appeared in Sec. 2.3. This discusses its definition, classification, levels and components of that term. In Sec. 2.4, the solar powered sensor nodes is motivated compared to nodes driven by other environmental energies. Accordingly, the geometry of solar energy is offered in Sec. 2.5. This section does not only show the types of solar radiation and solar angles, but also solar cells as harvesting devices.

2.1 Wireless sensor network

Wireless Sensor Network (WSN) is a group of wirelessly connected, specialized sensor platforms called sensor nodes. These nodes are distributed over a project area. The purpose of the nodes is to measure an environmental or physical parameters in order to use them for answering some questions in an industrial and research applications. Each individual node consists of energy storage unit, sensors, wireless transceiver, and microcontroller to control operations of the nodes. Different types of nodes may exist in a network, e.g. nodes primarily for sensing (named sensing nodes), nodes for relaying data (named routers), and nodes for facilitating data exchange with other networks, base stations as well as end user applications (named sink nodes). Typically, these sensor nodes are small and relatively cheap [16,24,25].

Sensor networks vary in size from a few nodes to thousands ones. Each node in the network is connected to one or more other nodes, allowing data to move through the network. Data move from the point of collection through the network until reaching a sink node where it can be retrieved for study. The retrieval process can be performed via manual download, wired transmission, or long range wireless transmission which is recently performed by mean of Internet [26–29]. However, Fig. 2.1 shows the structure of wireless sensor network [30].

Wireless sensor network 8

Internet (Cloud Service)

Gateway or Sensor Node Sink Node Base Station or End User Application Fig.2. 1. Structure of wireless sensor network

Depending on the application, wireless sensor nodes may powered by environmental energy like thermal, mechanical, solar, wind, and radio frequency energy. These energies are exploited as power source through energy harvesting systems like photovoltaic panels, wind turbines, thermoelectric generator…etc [26–29]. The inclusion of these systems may have a benefits over fixed and single energy sources. For example, the ability to replenish energy in the field, smaller batteries can be used. This may also lead to fewer maintenance visits, which can be costly depending on the remoteness of the site. Longer term deployments are also possible when energy harvesting is included as part of the sensor platform [17,18].

There are two major types of WSN applications: remote monitoring and mobile object location tracking. These types may be further divided into indoor and outdoor applications [16]. Monitoring applications require periodic sampling and transmission of data either at fixed intervals or in response to specific events. Some examples of remote monitoring include environmental and habitat monitoring [18,31–34], infrastructure monitoring, health monitoring, as well a number of smart grid related applications [35]. Examples of remote locations where WSN may be deployed include arctic locations [26], tropical regions [31], and inside glaciers [36].

There are a number of considerations that must be made for remote monitoring stations [26]:

Theoretical background 9

 Access to the deployment location may be restricted due to time, weather, cost, or any combination of these factors.  Weather conditions may reduce the effectiveness of energy storage devices (e.g., effect of temperature extremes on batteries).  Weather and other local conditions may reduce the effectiveness of energy harvesting devices (e.g., snow or dust covering solar panels).

Mobile object location tracking, while not the focus of this thesis finds applications in areas such as animal tracking for both agricultural and conservation purposes, child education [32,34], avalanche and fire rescue support, and support of product manufacturing and supply chain management [32].

2.2 Sensor node structure

It becomes clear now that the sensor node is the basic component of the network. This section handles the structure of the node itself. Although that many structures are available, only the basic structure will be discussed here. Actually, the basic structure is composed of two main units, energy harvesting unit and wireless sensor unit. Both of them are connected together as in Fig. 2.2 [37]. Additionally, each unit includes some its own components and has a specific task. However, the next subsections show a detailed explanation for each of them.

Communication Unit

Input Energy Output Regulation Regulation Microcontroller Harvesting DC/DC Converter Device MPPT

Storage Unit Measuring Unit

Energy Harvesting Unit Wireless Sensor Unit Fig.2. 2. Structure of sensor node

Energy management of WSN 10

2.2.1 Energy harvesting unit

Like any operating electrical system, a power source is needed to feed the load with energy. The task of this unit is to supply the wireless sensor unit with the needed energy to work. The energy is supplied here by an energy harvesting device (harvester). This device catch the ambient non-electric energy and convert it into electrical energy. For some individual cases, the system is fed with energy by two or more harvesters. These harvesters should work together in a complement way even the characteristics or the type of each are different. However, more operating challenges arise for this systems like power range, operating frequencies … etc. The harvesting unit also includes a storage unit to save the excess harvested energy. This energy can be considered as a reserve energy to be used when the power consumed is greater than the supplied power. This unit can also be used at emergencies like failures of the harvester or at the day-night cycle of a solar cell. Thus, it can operate in a clocked manner. For this unit, batteries or supercapacitors are often used [37]. The harvesters and the storage unit are connected to each other through a regulating units of input power like Maximum Power Point Tracker (MPPT), and output power like (DC/DC converter).

2.2.2 Wireless sensor unit

This unit is considered the load of the sensor node. Thus, it consists the basic “Measuring Unit” or sensor device. Another unit called “Communication Unit” is available in the wireless sensor unit. This unit works as a radio system for data transmission. The radio system typically has the highest energy consumption and therefore operates cyclically or event-oriented. A controlling unit is also exist in the sensor unit. This unit is indicated by “Microcontroller” and it is used to organize the operation of the both aforementioned units. Among all these three units, the microcontroller is the only unit that has a direct connection with the energy harvesting unit aforementioned before. For this reason, the microcontroller is the responsible body for distributing the energy come from the harvesting unit the measuring and communication units. Thus, it is described by many researchers as the core of the wireless sensor unit [37].

2.3 Energy management of WSN

This section deals with the term of energy management in WSN. This basically refers to the definition, classifications, levels and components of this term.

Theoretical background 11

2.3.1 Definition of energy management

Energy management in WSN is defined as the set of rules to manage various energy supply mechanisms and then efficient consumption of the provided energy in a sensor node. The overall aim should be to manage energy in such a way that no node becomes energy deficient and the network is operational perpetually. It is important for a sensor node to have an efficient energy management scheme for the limited source as well as the application requirement should be managed in accordance to the available energy source. Energy is considered as a scarce resource for a sensor node, specifically when a node is deployed in a remote region and once it depletes the available energy, it is almost impossible to provide supplant energy. Therefore, a balanced energy management between the supply and the load is required in order to avoid energy deficiency in a nodes [19,38].

Energy management is also needed to fulfil a balance between lifetime and quality of service. That is, increasing the quality of service may have a negative impact on the lifetime duration. Management strategies can seek to achieve this balance using factors like reduced measurement frequency, longer periods of time between transmissions, or lower network throughput. The complexity of the management scheme is related to the quality of service required by the application with more critical applications having more stringent requirements on uptime and measurement frequency [39,40].

2.3.2 Classifications of energy management

Energy management schemes in wireless sensor nodes can be classified on the basis of the nature of energy. Here, two main aspects or classes appear: management of energy provision and management of energy consumption [19].

The energy provision based schemes can be further classified into energy driven by batteries, energy harvested from the environment and energy transference based schemes. On the other side, management of energy consumption is broken down into three strategies: data driven, where energy usage is reduced by predicting data instead of measuring it, adapting the duty cycle to current conditions, and mobility based schemes involving mobile relays or sinks (Fig. 2.3) [19,41]. For some cases, combining an energy provision scheme with energy consumption one at the same time is necessary. The possibility of combination is an advantage whereby both sides can be managed. Thus, more effectiveness can be realized.

Energy management of WSN 12

Energy Management Schemes

Energy Provision Energy Consumption

Battery Data Duty Mobility Harvesting Transfer Driven Driven Cycle Based

Fig.2. 3. Schematic diagram of energy management schemes in WSN

The battery powered nodes are further classified on the basis of either fixed/replaceable battery supply or rechargeable battery. Similarly, the harvested energy also varies from different sources, of which, typical examples are solar, wind energy, thermal power ... etc. The recent advancement in the field of WSN’s evolved with the breakthrough of energy transference based supply. The sources such as magnetic resonance, reflected solar energy, microwaves / Radio Frequency (RF) Energy, and (Light Amplification by Stimulated Emission of Radiation) LASER power serves as the basics for energy transference based schemes [42]. The sole purpose of each technology is to provide an alternative energy sources to supply as much surplus energy as possible to improve network lifetime [19].

In data-driven approaches, several schemes are adopted to reduce data or to predict data, keeping accuracy at a certain level [19,43]. In duty cycling based schemes, nodes alternate between sleep and wake-up modes in order to achieve efficient energy utilization [44–49]. On the other hand, mobility based schemes use a mobile sink or a mobile relay depending on its behavior, which can be used as part of the environment or part of the network [50–53]. However, this thesis is dealing with energy consumption management schemes where the sensors powered by environmental harvested energy. In more details, with that data driven ones.

2.3.3 Levels of energy management

Three levels can be considered when managing energy within the WSN: the microcontroller level, node level, and network level (Fig. 2.4) [19]. At the microcontroller level, energy is managed through proper selection of the microcontroller itself as well as dynamic voltage and frequency scaling. These

Theoretical background 13 techniques require hardware created with these features, which means that they must be considered very early on during development and may be difficult to change afterwards. Also, selection of appropriate energy storage technologies to avoid unnecessary losses, as well technologies to reduce energy conversion losses (e.g., maximum power point tracking for photovoltaic panels) should be considered in the early design stage [27,54].

Node level energy management includes tools such as adaptive sensing rates where measurements are taken considering the amount of energy available for use or scaled based on changing variability of the target variable. The bulk of energy consumption in a sensor node is due to the wireless communication, making the reduction of wireless transmissions and idle listening time an important node level power management techniques [16,55]. By reducing the number of transmissions a node sends to the rest of the network, the energy used by the entire network is reduced. This may take a number of different forms, from very simple schemes where transmissions are simply not sent, to more advanced techniques where redundant information is reduced through prediction of future values [56,57]. Sampling may also be reduced while recognizing the increase in error associated with less frequent measurements [58].

Microcontroller Node Level Level Energy Management

Network Level

Fig.2. 4. Levels of energy management in WSN

At the network level, energy management can be realized through schemes like communication scheduling and intelligent, energy-aware message routing, all of which reduce the number of required wireless transmissions [40,55,59]. For WSN with enough node density to support it, clustering can be used to improve deployment

Energy management of WSN 14 duration [60,61]. In these network topologies, nodes are clustered into different groups. Each cluster consists of at least one cluster head and a number of non-cluster heads. Cluster heads handle the processing and forwarding of data to the network base station. With this scheme, some of the energy consumption of non-cluster heads is shifted to the cluster head. This makes selection of the cluster head crucial, as energy harvesting opportunities and the power requirements of transmission distance to the base station must be considered. Base stations may also be moved, and a method of determining the optimal position is presented in [62]. Relocation of network base stations can reduce energy use and increase network lifetime by locating the base station such that the power intensity of transmission may be reduced.

2.3.4 Components of energy management

Energy management is an important part to be considered in the wireless sensor nodes. This part requires availability of components to perform the task of management. Fig. 2.5 shows in the red box the components that contribute to the energy management [37,63]. In the figure, there is a DC/DC converter which is used to rectify different power potentials from batteries and harvesters. In addition, there is a Maximum Power Point Tracking (MPPT) unit. This unit is used basically to maximize the harvested power by the harvester device. As a principle, DC/DC converter and (MPPT) unit are considered as management tools of supplied energy.

Communication Unit

Input Energy Output Regulation Regulation Microcontroller Harvesting DC/DC Converter Device MPPT

Storage Unit Measuring Unit

Energy Harvesting Unit Wireless Sensor Unit

Fig.2. 5. Components of energy management in wireless sensor nodes

Theoretical background 15

On the side, energy consumption can also be managed. To this end, the microcontroller is used. The microcontroller monitors the energetic state of the components, determines operating conditions, and distributes power between load, harvester and storage. Thus, it able to switch the system between high and low power consumption within a static predefined operational duty cycles. For example, the radio system consumes more energy than other units. Thus, an enough energy should be available before the cycle of data transmission. This can be examined at the beginning of duty cycles by different prediction algorithms [37,63]. After that, an energy management strategy, like the neutrality scheme in [54] is applied in order to choose a suitable state or mode to operate.

In the literature, the term “Energy Management” is thus variant and dependent on the complexity of the overall structure. It ranges from a simple one DC/DC converter design with power adjustment up to the energy-dependent intelligent control. However, the scientists and researchers looks to the energy management in general that it should have the following characteristics [64]:

1. Allow a regulation of different voltage levels, e.g. Source input, Memory load or long-term memory buffer. 2. Have a power adjustment for the efficient use of the source(s). 3. Able to know the individual instantaneous states like the state of charge of the batteries and the consumption of the load. 4. Able to intelligently manage energy flows (this is also different operating states necessary as a decision criterion). 5. Save energy and work. 6. Have a functionality for advanced planning.

2.3.5 Principles of energy management

As the energy management schemes aim to enhance the functionality, operational capability and prolonging the lifetime of a wireless sensor system, they all should rely on a basic principles to achieve this target. As a rule, two basic principles are available in these schemes. These principles are [19]:

1) Maximizing supplied energy: This principle can be fulfilled by the components of the harvesting unit. Mainly, selecting the most suitable energy converter is considered the fundamental procedure. The type of energy source, the efficiency, the spectral sensitivity, the robustness and the ability to convert artificial light should be considered as a

Solar powered sensor nodes 16

criteria during the process of selection [65]. Additionally, utilizing a MPPT is another procedure for this principle. The tracker offers an improvement over the traditional converters [66]. Lastly, the storage units like batteries and supercapacitors. These units should be selected to store as much energy as possible without a negative effect due the large size and large losses [67].

2) Minimizing consumption energy: As this principle is related to operating the wireless sensor unit efficiently “lossless unit”, the microcontroller is the responsible component about fulfilling this principle. The microcontroller should be selected to be energy saving one [68–70]. It should also be able to implement a suitable mechanism of operation to ensure as lower energy consumption as possible. This can be achieved by the prediction of solar energy as well as a cyclic operation for the measurement/transmission processes [71,72]

2.4 Solar powered sensor nodes

The sensor node, in its traditional form, which is powered only by fixed batteries are no longer feasible. This is due to the frequent and costly replacement and maintenance visits. These visits require more human efforts to reach the places of these nodes which in some cases not accessed. Thus, exploiting an environmental energy to support the batteries became reasonable and promising solution. This is attributed mainly to the possibility of suppling the node with energy in the field. Thus, the replacement maintenance visits become limited [73,74].

As a specific case, this thesis focus on solar powered sensor nodes. This attributed to the features of the solar energy in comparison with other environmental sources. From one side, it has large power density (roughly, 15 푊/푐푚2). Accordingly, it allows the applicability within outdoor environment. From other side, the size of solar panels which accompanied by low cost is helpful and strongly required by the manufactures of sensor node and the implementers of the networks [20–22].

Understanding solar powered sensor nodes requires taking a look on the solar energy as a renewable energy source. Especially that the microcontroller needs to predict the upcoming solar energy to control the energy consumption in the node. However, the next section shows a detailed information about the solar energy, its geometry and solar cells as energy harvesting device.

Theoretical background 17

2.5 Geometry of solar energy

The solar energy can be extracted from the sun’s radiation. Actually, the radiation has different types. These types are correlated to each other through a mathematical relations and solar angles.

2.5.1 Types of solar radiation

The Global Solar Radiation (퐺푆푅) that comes from the sun into a receiver surface is composed of two basic components, Diffuse Radiation (퐷푅) and Direct (Beam) Radiation (퐵푅). As a definition, Diffuse Radiation (퐷푅) is the radiation that is scattered or reflected by atmospheric components and factors. The term of Direct Radiation (퐵푅) refers to the extraterrestrial radiation above the atmosphere minus the atmospheric losses due to absorption and scattering i.e. the fallen unhindered solar radiation. Fig. 2.6 shows a graphical explanation of these two components or types [75].

퐵푅

Atmosphere

퐷푅 퐷푅

Earth

Fig.2. 6. Types of solar radiation

Mathematically, the global solar radiation is expressed as in (2.1). From this equation, the direct radiation is given by the position of the sun over the day, which is described by the zenith angle (휃푧). Thus, 퐵푅 will drops to zero when the angle reaches 90°.

퐺푆푅 = 퐷푅 + 퐵푅 푐표푠 휃푧 (2.1)

Additionally, the diffuse radiation and the direct radiation can be linked to each other, as one of them is part of the other (2.2) using a constant value (푐) .

Geometry of solar energy 18

퐷푅 = 푐 × 퐵푅 (2.2)

Fig. 2.7 shows two different patterns of daily solar radiation. The direct radiation, which is mentioned before as the difference between the global and diffuse radiations, is represented with the yellow color. The pattern in (a) shows that the direct radiation is small compared to the diffuse one. This is mainly attributed to the presence of high density cloud cover in the sky over the whole day. In contrast, (b) pattern refers to clear and semi cloudless sky. Thus, the global radiation is composed here largely from direct radiation more than the diffuse one. Additionally, an increase in 퐺푆푅 values starts from the sunrise time until the mid-day. A decreasing behavior for these values begins after that until the sunset time. These behaviors relates to change the position of the sun

(change of 휃푧).

(a) (b)

Fig.2. 7. Solar radiation patterns in (a) cloudy day (b) clear sky day

Although that the solar radiation is described with three different types, the global solar radiation 퐺푆푅 is the main target for many researches in their works. This is because 퐺푆푅 consists the other two types of radiation. Thus, it is more comprehensive. Especially, when the studies focus more on the falling radiation on a solar cells.

In general, the global and diffuse radiations are practically measured, separately or together, by accurate measuring device called “Pyranometer”. The device measures the incoming solar radiation expressed as electric power. Basically, the device includes a thermopile to catch the temperature and use it later to find the corresponding

Theoretical background 19 thermoelectric voltage which is used directly to calculate the radiation. However, a detailed description about the pyranometr’s components is available in Appendix 1.

2.5.2 Solar angles

As a fact, the solar radiation changes from one time to another. This change happens seasonally, daily and even every minute. This change is mainly due to the changing of some solar angles in addition to other factors. In this section, these angles will be discussed in details and the other factors will be discussed in the upcoming sections. These angles are variant and correlated to each other. For better understating these angles, the beginning will be with the declination angle as it can be used to calculate the other angles.

2.5.2.1 Declination angle (휹)

The declination determines the position of the Earth in the system of tropical coordinates. Consequently, an angle called declination's angle (훿) arises according to the rotation of the Earth around the sun. This angle is measured north or south of the celestial equator, along the hour circle passing through the point in question. The declination angle is the same for the whole globe at any given day. This means that it does not change with location in the Earth. The declination angle is calculated by the mathematical formula in (2.3).

휋 284+푑 훿 = 23.45 푠푖푛 [2휋 ( 표푟푑푒푟)] (2.3) 180 365

st st where 푑표푟푑푒푟 is the order of the day in the year (from 1 at 1 Jan to 365 at 31 Dec). According to this formula, 훿 changes from -23.45° at winter solstice to 23.45° at summer solstice. When the earth passes in the vernal and autumnal equinoxes, no declination occurs (훿 = 0°). Fig. 2.8 shows the change of the Earth’s position around the sun with its corresponding declination angle throughout a year. This change draws a sinusoidal shape during the year. Since this change is very slight from one day to another, it effect can be neglected. For this reason, the declination angle is described to be of seasonal effect on solar radiation [75].

It is worth mentioning that the declination angle can be used to describe any other objects in the tropical coordinates other than the Earth, like the planets. However, the researchers are interested with the values of this angle for the Earth as it is the working environment for the harvesting solar cells and wireless sensor nodes.

Geometry of solar energy 20

Vernal Equinox (21 Mar) 훿 = 0°

훿 = −23.45° Sun 훿 =23.45°

Summer Solstice Winter Solstice (21 Jun) (21 Dec)

Autumnal Equinox (21 Sep) 훿 = 0°

Fig.2. 8. Changing of declination angle over a year

2.5.2.2 Zenith angle (휽풛) The daily path of the sun and the resulting angular relationships are shown in Fig. 2.9.

The zenith angle (휃푧) of the sun lies between the zenith of the viewing area and the sun. The azimuth angle is denoted by 휓, the elevation angle is ℎ , the solar declination is 훿, the geographic latitude is 휑 and 휔 describes the angle of time. Using these variables,

휃푧 can be obtained mathematically from the relation in (2.4) [76].

푐표푠 휃푧 = 푠푖푛 훿 푠푖푛 휑 + 푐표푠 훿 푐표푠 휑 푐표푠 휔 = 푠푖푛 ℎ (2.4)

−1 휃푧 = 푐표푠 (푠푖푛 ℎ) (2.5)

Theoretical background 21

Zenith

Sun’s path

W 휃푧 Center of the Earth ℎ

−휔 휓 S +휔 N

E

Projection of sun’s path

Fig.2. 9. Angular relationships of the solar day

2.5.2.3 Azimuth angle (흍)

The azimuth angle is the angle between the projected vector of the sun’s position and a reference vector (North) on the reference plane. The azimuth angle is calculated in (2.6). This angle is 0° at the north and 180° at the south [75,76].

푠푖푛 ℎ 푠푖푛 휑−푠푖푛 훿 푐표푠 휓 = (2.6) 푐표푠 훿 푐표푠 휑

2.5.2.4 Time angle (흎)

The time angle is positive during the morning, reduces to zero at noon and becomes increasingly negative as the afternoon progresses. Two equations can be used to calculate this angle when various angles are known (note that 훿 changes from day to day, ℎ and 휓 change with time throughout the day):

푐표푠 ℎ 푠푖푛 휓 푠푖푛 휔 = − (2.7) 푐표푠 훿

푠푖푛 ℎ −푠푖푛 훿 푠푖푛 휑 푠푖푛 휔 = (2.8) 푐표푠 훿푐표푠 휑

Geometry of solar energy 22

At solar noon, the time angle equals zero and since the time angle changes at 15° per hour it is a simple matter to calculate the time angle at any time of day. The time angles at sunrise and sunset (휔푠) are very useful quantities to know. Numerically, these two values have the same value. However the sunrise angle is negative and the sunset angle is positive. Both can be calculated from (2.9):

푐표푠 휔푠 = − 푡푎푛 휑 푡푎푛 훿 (2.9)

This equation is derived by substituting ℎ = 0 into equation (2.4). Time angle can be used to find the number of daylight hours (푁퐷푎푦푙푖푔ℎ푡) for a particular day using the next equation, where 휔푠 is in radians:

2휔 180 푁 = 푠 × 퐷푎푦푙푖푔ℎ푡 15 휋 (2.10)

Note that there are always 4380 hours of daylight per year (non-leap years) everywhere on the globe. For equation (2.9) beyond 휑 = ± 66.55°: tan 훿 − tan 휑 ≥ 1 there is no sunset, i.e. 24 hours of daylight; tan 훿 − tan 휑 ≤ 1 there is no sunrise, i.e. 24 hours of darkness [75,76].

2.5.3 Influencers of solar energy

There are some factors affect the availability of solar energy on the Earth. These factors are not only related to the position of the sun and the solar surfaces, but also to nature of the radiation and the atmospheric factors. Some of them a negative effect (attenuation), while the others are positive (intension). However, a detailed information about these different factors are listed in this section.

2.5.3.1 Atmospheric attenuators

The direct radiation is exposed to different atmospheric factors. Each one of these atmospheric factors contributes to transmit, reflect or absorb a part of the falling radiation through its own coefficient. These coefficients are expressed as a function of the wavelength(휆). However, the most important factors are:

 Mixed gases:

The most widespread gases are oxygen (O2), Carbone dioxide (CO2) and water (H2O). The effect of oxygen and carbon dioxide is small compared to water. This is attributed to the variation of water percentage in the air greatly due to the

Theoretical background 23

weather. Additionally, the concentration of these gases changes over the day

according to the change of 휃푧. At a lower zenith angle, high reduction of the direct radiation component occurs. However, the transmission coefficient for the factor of gases is calculated as in (2.11).

퐴푃푠푡푎 −1.41 . 휎퐺 (휆) . 푚 . 퐴푃표 휏 (휆) = 푒푥푝 ( 0.45) (2.11) 퐺 퐴푃푠푡푎 [1+118.93 . 휎퐺(휆) . 푚 . ] 퐴푃표

where 휎퐺 (휆) is the spectral absorption coefficient, 푚 is the air mass, 퐴푃푠푡푎 and 퐴푃표 corresponds to the atmospheric pressure at the standard condition and at sea level, respectively [77].

 Scattering particles: The particles like dust, salt, soot and sand also contribute to a specific absorption. The transmission coefficient that due to the presence of particles in the aerosols are described by:

휆 −훼퐴 휏 (휆) = 푒푥푝 (−훽 . [ ] . 푚 ) (2.12) 푆푃 퐴 1000 푛푚 푆푃

where 훽퐴 is the extinction coefficient and 훼퐴 is the particle size [78]. Both of these coefficients are obtained from Table 2.1.

Table 2. 1. Values of 훽퐴 and 훼퐴 for various atmospheric states [79]

Atmosphere 휷푨 휶푨 Visibility (km) Clean 0.00 1.3 340 Clear 0.10 1.3 28 Cloudy 0.20 1.3 11 Very cloudy 0.40 1.3 <5

 Water steam: Water steam is the main cause of absorption in the higher wavelength range. The transmission coefficient is formed from the water steam content of the

atmosphere 푊푠푡푒푎푚, the spectral absorption coefficient 휎푊(휆) and the relative optical water steam mass 푚푊.

−0.2385 . 휎푊 (휆) . 푊푠푡푒푎푚 . 푚푊 휏푊(휆) = 푒푥푝 ( 0.45) (2.13) [1+118.93 . 휎푊(휆) . 푊푠푡푒푎푚 . 푚푊]

Geometry of solar energy 24

The transmission coefficients differ from one geographical location to another according to different aspects like (proximity to water, rural or urban areas, longitude, latitude … etc.) [77]. The whole aforementioned transmission coefficients can be collected into one common coefficient 휏(λ), which is a multiplication of all coefficients together [80].

휏(휆) = 휏퐺(휆). 휏푆푃(휆). 휏푊(휆) (2.14)

Thus, the direct radiation after the reduction or attenuation (퐵푅λ) is expressed as:

퐵푅휆 = 퐵푅. 휏(휆) (2.15)

There are another factors like ozone concentration, turbidity, and effect of smaller particles that do not discussed here.

2.5.3.2 Air mass effect

The air mass coefficient defines the length of direct optical path through the Earth's atmosphere, expressed as a ratio relative to the length of vertically path upwards, i.e. at the zenith (Fig. 2.10). The air mass coefficient can be used to help characterize the solar spectrum after solar radiation has traveled through the atmosphere. The basis for that is the solar constant, which corresponds to the annual average incoming radiation power outside the Earth's atmosphere (1367 푊/푚2) [81].

The air mass coefficient is often referred by a syntax "AM" followed by a number. This number indicated to the ratio mention before. "AM 1.0" is the air mass coefficient at the zenith, "AM 1.5" is the air mass coefficient at angle 48.2° from the zenith and "AM 2.0" occurs at angle 60.1° from the zenith. The larger the angle, the longer the distance the radiation travels within the Earth's atmosphere. With a larger path in the atmosphere, the attenuation of incoming radiation is also increased. However, the AM coefficient is calculated by:

AM = 푠푒푐 휃푧 (2.16)

This equation has been changed over the time to:

1 AM = (2.17) 푎(ℎ+푏)푐+푠푖푛 ℎ

Theoretical background 25

The values of the variables are: a = 0.50572, b = 6.07995 and c = 1.6364. The relative error of this approximation is given as less than 0.5% for a minimum error. For further calculations in the following chapters, "AM" will be denoted as 푚.

The "AM 1.5" has a particular importance because it is used for testing, power determination and comparison of solar modules according to uniform standard test conditions. However, the maximum visible range for the spectrum "AM 1.5" is about a third lower than for the spectrum which is not attenuated without atmospheric exposure "AM 0" [82].

Zenith

AM1.0 Atmosphere

60.1° 48.2°

Earth

Fig.2. 10. Path length of different air mass coefficients

2.5.3.3 Multiple reflections effect

In general, solar cells can absorb part of the whole direct and diffuse radiations that fall on them, where the other part is reflected. However, these reflections may reflect again and again on the same surface when they find a cover (Fig. 2.11).

For a times of reflections 푛푟푒푓, the total absorbed direct radiation 퐵푅푎푏푠 can be expressed using (2.18). This equation considers the absorptivity of the absorber and the glass cover 휀푎 and 휀푔 , the reflectivity of the absorber and the glass cover 휌푎 and 휌푔, respectively.

2 3 푛푟푒푓 퐵푅푎푏푠 = 퐵푅. 휀푔 . 휀푎 (1 + 휌푎 휌푔 + (휌푎 휌푔) + (휌푎 휌푔) + ⋯ + (휌푎 휌푔) ) (2.18)

Geometry of solar energy 26

By considering the effect of the aforementioned transmission coefficients in the previous equation:

2 3 푛푟푒푓 퐵푅푎푏푠 = 퐵푅λ. 휀푔 . 휀푎 (1 + 휌푎 휌푔 + (휌푎 휌푔) + (휌푎 휌푔) + ⋯ + (휌푎 휌푔) ) (2.19)

This equation can be rewritten as:

퐵푅λ . 휀𝑔 . 휀푎 퐵푅푎푏푠 = (2.20) 1− 휌푎 휌𝑔

For each absorber, the absorptivity and reflectivity are related to each other by:

휀푎 + 휌푎 = 1 (2.21)

Also, for the cover:

휀푔 + 휌푔 = 1 (2.22)

Cover 휺품 , 흆품

Absorber

휺풂, 흆풂 퐵푅. 휀푔 . 휀푎 퐵푅. 휀푔. 휀푎. 휌푎. 휌푔 2 퐵푅. 휀푔. 휀푎. (휌푎 휌푔)

Fig.2. 11. Reflections of direct radiation on a solar surface (absorber)

The relationships for the diffuse radiation are more complex, since they depend on the perspective, isotropic or anisotropic distribution. The reflected diffuse radiation is therefore usually given by a factor only [83].

Theoretical background 27

2.5.3.4 Tilted surface effect

In reality, the solar cells are applied to be tilted with an angle from the horizontal or the ground (Fig. 2.12). The tilt angle (휁) has a significant effect on the direct, diffuse and the reflected radiations which in their role influence the global solar radiation. Consequently, the global solar radiation over a tilted cell can be expressed as a summation of three terms as next [84]:

퐺푆푅푡푖푙푡푒푑 = 퐵푅푡푖푙푡푒푑 + 퐷푅푡푖푙푡푒푑 + 푅퐸푡푖푙푡푒푑 (2.23) where,

퐵푅푡푖푙푡푒푑 : Direct radiation at tilted cell. 퐷푅푡푖푙푡푒푑 : Diffuse radiation at tilted cell. 푅퐸푡푖푙푡푒푑 : Reflected radiation at tilted cell.

Zenith North

휃푧

휁 West ℎ

East

South Fig.2. 12. Solar angles for tilted solar cell

The direct radiation at a tilted cell can be expressed using pure geometric parameter depends on the zenith and title angles as in (2.24).

cos 휁 퐵푅푡푖푙푡푒푑 = 퐵푅. (2.24) cos 휃푧

For calculating the diffuse radiation at tilted cell, an isotropic distribution over the hemisphere is considered. Thus, it depends only on the tilt angle and the diffuse radiation as shown in (2.25).

Geometry of solar energy 28

1+cos 휁 퐷푅 = 퐷푅. ( ) (2.25) 푡푖푙푡푒푑 2

The reflected radiation is dependent on the ground’s ability to reflect. The ground reflectivity is a property expressed by the albedo factor (휌퐺). According to Table 2.2, albedo factor ranges from 0.1 for asphalt paved road to 0.9 for snow ground. Given the albedo, the reflected term can be calculated from:

1−cos 훾 푅퐸 = 휌 (퐵푅 + 퐷푅) ( ) (2.26) 푡푖푙푡푒푑 퐺 2

Table 2. 2. Albedo factor values of variant grounds type [85]

Ground’s type Albedo factor (흆푮) Lawn 0.205 Untitled field 0.26 Naked ground 0.17 Weather beaten concrete 0.3 asphalt 0.15 Fresh snow 0.85 Old snow 0.58

2.5.4 Solar cells

Solar energy can be harvested by a group of connected solar cells. These solar cells as harvesting device is available in the market with small and large scales. For the application of sensor node, small scale cells like that one in Fig. 2.13 [86] should be used. This is to be compatible with the system’s design i.e. suitable for the utilized battery and to keep the small size of the node. Regarding the connection of the cells, it can be series or parallel connection, which is performed according to the desired purpose from the system. The connection here requires considering the operating parameters of the cells like the voltage and current. The actual efficiency of these cells do not exceed 20%.

Theoretical background 29

Fig.2. 13. Real small scale solar cell

2.5.4.1 Electrical equivalent circuit of solar cell

Each solar cell can be represented by equivalent circuit consisting of current source 퐼푝ℎ

, shunt resistor 푅푠ℎ for drawing a leakage current 퐼푠ℎ , a series resistor 푅푠 for drawing the output current 퐼푃푉 of the cell and diode connected in parallel with the aforementioned current source as in Fig. 2.14 [87].

푅푠 퐼푃푉 + 퐼 퐼퐷 푠ℎ +

퐷 푅 퐼푝ℎ 푉퐷 푠ℎ 푉푃푉 −

−

Fig.2. 14. Equivalent circuit of solar cell

An equivalent circuit of two parallel diodes (two diode model) is found in Appendix 2 [88]. The second diode is used to draw the nonlinear behavior. However, the basis for the consideration of current and voltage is the Shockley equation in (2.27) that describes the behavior in the ideal diode:

푞.푉퐷 퐸 .퐵 .푇 퐼퐷 = 퐼푠 (푒 푐 푧 푑 − 1) (2.27)

Geometry of solar energy 30

where,

퐼퐷 and 푉퐷 : are the diode current and voltage, respectively. −14 퐼푠 : is the saturation current (in order of 10 ). 푞 : is the electrical charge of the electron (= 1.6 × 10−19 Coulomb).

퐸푐 : is the emission coefficient (=1 for indirect semiconductor like Si & Ge, =2 for direct semiconductor like GaAs & InP). −23 퐵푧 : is the Boltzmann’s constant (= 1.38 × 10 J/K). 푇푑 : is the diode temperature in Kelvin.

퐵 . 푇 The ratio 푧 푑 is known as the thermal voltage 푉 which equals 25.9 mV (at the room 푞 푡ℎ temperature 300 Kelvin). The output current derived from the solar cell is:

퐼푃푉 = 퐼푝ℎ − 퐼퐷 − 퐼푠ℎ (2.28)

By substituting the currents with their expressions:

푞.(푉푃푉+퐼푃푉푅푠) (푉 +퐼 푅 ) 퐸푐.퐵푧.푇 푃푉 푃푉 푠 퐼푃푉 = 퐼푝ℎ − 퐼푠 (푒 푑 − 1) − (2.29) 푅푠ℎ

2.5.4.2 Performance of solar cell

The voltage / current characteristic curve of a solar cell behaves nonlinearly as it is clear in Fig. 2.15. This is the reason that the corresponding derived power is also nonlinear. From the figure, there is only one point where the maximum power can be obtained. Better cell performance can be reached when it works at the maximum power point.

However, the relationship between open circuit voltage (푉푂퐶 ) and short-circuit current (퐼푆퐶) with the point of maximum power can be expressed by the Fill Factor (퐹퐹) in (2.30) [89]:

푉 . 퐼 푃 퐹퐹 = 푀푃푃 푀푃푃 = 푀푃푃 (2.30) 푉표푐 . 퐼푠푐 푉표푐 . 퐼푠푐

Where,

퐼푀푃푃 and 푉푀푃푃 are respectively the current and voltage at the maximum power operating point. 푃푀푃푃 is the power at the maximum operating point.

Theoretical background 31

퐼 푠푐

퐼푀푃푃 푃푀푃푃

퐼 Current Power

퐼 푃

Power

Current

푉 푉 푀푃푃 표푐 Voltage

Fig.2. 15. Voltage / current characteristics of a solar cell 푉 푉

During the day, the sunlight and temperature change. These changes affects the characteristic curves of the solar cell (Appendix 3). Consequently, the operating point will change too. An adjustment for this point should be fulfilled in order to derive the maximum power from the cell. For this purpose, Maximum Power Point Tracking (MPPT) methods have been created. These methods differ from each other in the way that the maximum power point is determined, in the parameters required for this and in the required computing power or storage capacity [90].

State of the art for solar energy prediction in wireless sensor nodes 33

3 State of the art for solar energy prediction in wireless

sensor nodes

This chapter provides a state of the art about different solar energy prediction algorithms utilized with wireless sensor nodes. This chapter starts with the classification of these algorithms in Sec. 3.1. This includes the stochastic, statistical and Artificial Intelligence (AI) algorithms. In Sec. 3.2, the historical progress of these algorithms is shown. Sec. 3.3 handles the stochastic algorithms and its basic principle. Sec. 3.4 explains the statistical algorithms in details. This section includes three different categories, the exponentially weighted, the weather conditioned and the profile energy. The Artificial Intelligence (AI) algorithms are treated in Sec. 3.5. After that, a performance comparison for all these algorithms is discussed Sec. 3.6. Lastly, the methodology adopted for the provided work of this thesis is offered in Sec. 3.7.

3.1 Classification of solar energy prediction algorithms

There is a variety in the solar energy prediction algorithms that utilized with sensor nodes. These algorithms can be classified mainly into three main categories as in the schematic diagram shown in Fig. 3.1. Each category includes a group of algorithms. The criteria considered for the classification is the nature of the prediction process itself [91]. The following is an explanation for these categories:

 Stochastic algorithms: These algorithms refer to that algorithms in which a random process is occurred for the prediction. Thus, the probabilities happened between different energy states will be used here [92].  Statistical algorithms: These algorithms use a mathematical analysis of a recent observations registered by the system considering a weighting factors. These algorithms are numerus and some of them are enhanced versions of the basic ones [93].

History of solar energy prediction algorithms 34

 Artificial Intelligence (AI) algorithms: These algorithms depend on the machine to make the prediction through a prior training process on a real measured data set. Thus, they are smart compared to the previous stochastic and statistical ones [91].

Prediction Algorithms of Solar Energy in Wireless Sensor Nodes

Stochastic Statistical AI Algorithms Algorithms Algorithms

ASIM Fuzzy Logic Neural Networks

Exponentially Weather Profile Weighted Conditioned Energy Algorithms Algorithms Algorithms

EWMA WCMA Pro-Energy ASEA WCMA-PDR IPro-Energy SEP-DR WCSMA Pro-Energy-VLT SEPAD AR-WCMA D-WCMA UD-WCMA

Fig.3. 1. Schematic diagram for prediction algorithms classification

3.2 History of solar energy prediction algorithms

The prediction of solar energy in the wireless sensor nodes started in 2007 with a statistical algorithm called Exponentially Weighted Moving Average (EWMA). The prediction is developed from that time until now. This development was being performed by creating new algorithms or enhancing an existing ones. Additionally, the development covered the all categories or classes mentioned in Sec. 3.1. Although that the developed algorithms differ in the accuracy, the intelligence, the execution

State of the art for solar energy prediction in wireless sensor nodes 35 time and the needed memory space, the chronological order of that development does not accompanied by ascending order of all these aspects. However, Fig. 3.2 shows the development of that algorithms until the year 2017.

Year UD-WCMA (Universal Dynamic Weather Conditioned Moving Average) 2017 D-WCMA (Dynamic Weather Conditioned

Moving Average)

IPro-Energy (Improved Profile Energy)

Pro-Energy-VLT (Profile Energy-Variable Length Time)

AR-WCMA (Auto Regression- Weather Conditioned 2016 Moving Average)

SEP-DR (Solar Energy Prediction based on Daily Ratio)

2015 ASIM (Accurate Solar Irradiance Model)

SEPAD (Solar Energy Prediction based on 2012 Additive decomposition) Pro-Energy (Profile Energy)

2011 ASEA (Accurate Solar Energy Allocation)

WCSMA (Weather Conditioned Selective Moving Average) 2010 WCMA-PDR (Weather Conditioned Moving Average

- Phase Displacement Regulator)

2009 WCMA (Weather Conditioned Moving Average)

2007 EWMA (Exponentially Weighted Moving Average)

Fig.3. 2. Historical progress of solar energy prediction algorithms in wireless sensor nodes

Stochastic prediction algorithms 36

3.3 Stochastic prediction algorithms

The stochastic algorithms are considered the weakest among all prediction algorithms in terms of accuracy. The basic principle of the prediction in these algorithms is the “Markov Chains”. These chains describe the sequence of possible energy state in which the probability of each upcoming state depends on the previous attained state [94]. For these algorithms, a predefined discrete energy states are specified from a real measured data set. The chain is created using both the attainable state and the state of transition probabilities (Fig 3.3). Normally, the possible states are generated by dividing the training data set into fixed size bins. Each bin represents a unique state. So, the number of the sates is found by dividing the highest energy state in the data set over the bin size. The transition probabilities are also obtained using the training data [95,96].

푃 푖푗

State State

푃 i j 푖푖 푃푗푗 푃푗푖

푃푖푘 푃푘푗 푃푗푘 푃푘푖

State k

푃푘푘

Fig.3. 3. Transition probabilities of the Markov chain

For example, the probability matrix or transition matrix for the three states in Fig 3.3 can be written as: 푖 푗 푘 푖 푃푖푖 푃푖푗 푃푖푘 푃푚푎푡 = 푗 [푃푗푖 푃푗푗 푃푗푘 ] (3.1) 푘 푃푘푖 푃푘푗 푃푘푘

Where 푖, 푗, 푘 are the states and 푃푖푗 is the probability of transition from state 푖 to state 푗.

State of the art for solar energy prediction in wireless sensor nodes 37

Using this matrix, the next state 푥푛+1 can be calculated for from the following equations. In this equation 푥푛 refers to the current state.

푛+1 푛 푥 = 푥 . 푃푚푎푡 (3.2)

The stochastic row vector or the stochastic chain can also be calculated using this matrix. For example, 푥푛+3 which is the energy state after three steps is expressed as follow:

푛+3 푛+2 푥 = 푥 . 푃푚푎푡 (3.3) 푛+1 = (푥 ). 푃푚푎푡). 푃푚푎푡 푛 2 = ((푥 . 푃푚푎푡). 푃푚푎푡 푛 3 = 푥 . 푃푚푎푡

The number of the previous energy states considered in the calculations refer the order of the “Marko Chains”. The chains of higher order increase the accuracy of the prediction [96]. However, these algorithms has a drawback of discreet states which are mostly do not fit the actual readings. The most common stochastic algorithm that use these chains is called ASIM (Accurate Solar Irradiance Model) [92].

3.4 Statistical prediction algorithms

There are three categories of the statistical algorithms. A detailed clarifications for these algorithms are found in the following subsections.

3.4.1 Exponentially weighted algorithms

This category includes four prediction algorithms. One of them is basic and the others are improved versions of it.

3.4.1.1 EWMA

Exponentially Weighted Moving Average (EWMA) is the simplest, oldest and most popular algorithm in the literature for predicting of energy in wireless sensors nodes. It depends on the diurnal cycle of the solar radiation. Thus, it assumes that the generated energy at a predictable time slot (푛 + 1) in the current day (푑) will be similar to the generated energy at the corresponding time slots of the previous days. The main principle of EWMA is to adapt the seasonal changes by maintaining the amount of

Statistical prediction algorithms 38 harvestable energy at specific time slot as a weighted average of the harvested amounts of previous days. In other words, EWMA algorithm depends on the historical data about the harvested generation profiles. Equation (3.4) and Fig. 3.4 show respectively the mathematical and graphical representation for EWMA algorithm.

퐸(푑, 푛 + 1) = 훼 퐻(푑 − 1, 푛 + 1) + (1 − 훼) 퐻(푑 − 2, 푛 + 1) (3.4)

1 − 훼 +

훼

Fig.3. 4. Graphical representation of EWMA algorithm

In order to give more effect to the recent past values, a weighting factor (훼) is used. The value of this factor reduces exponentially and ranges between zero and one(0 < 훼 < 1). This algorithm works perfectly under consistent weather conditions. The main drawback of this approach appears when a temporary weather conditions occurred. (i.e. when weather changes from sunny to cloudy or vice versa, the performance collapses and large prediction error occurs) [97].

3.4.1.2 ASEA

Accurate Solar Energy Allocation (ASEA) uses the foundations of EWMA in order to provide optimal allocation of the periodically harvested solar energy in sensor nodes. This algorithm minimizes the variations in allocated energy in each time slot. To do this, it modifies the EWMA to cope with its drawback. ASEA introduces a new parameter called 휖 into equation (3.4). This parameter reflects the current solar conditions. The modified equation can be given as in (3.5):

퐸(푑, 푛 + 1) = 퐸퐸푊푀퐴(푑, 푛 + 1) . 휖 (3.5)

퐻(푑,푛) 휖 = (3.6) 퐸퐸푊푀퐴(푑,푛)

The parameter (휖) represents the ratio between the actual amount of energy harvested

퐻(푑, 푛) and the energy predicted by EWMA slot 퐸퐸푊푀퐴(푑, 푛) for the previous. The expected energy, 퐸(푑, 푛 + 1) is calculated by multiplying 휀 with the energy expectation by EWMA. This value is calculated at the beginning of each time slot. ASEA considers only the condition in the previous slot, which might result in significant prediction

State of the art for solar energy prediction in wireless sensor nodes 39 errors for short term varying weather conditions. A temporary weather change in the current slot would lead to inaccurate prediction for the next slot [98].

3.4.1.3 SEP-DR

Solar Energy Prediction based on Daily Ratio (SEP-DR) algorithm uses the most recent energy observations in the current day. This algorithm assumes that solar energy behaves a cyclic periodic energy in which the time separates into equal lengths slots repeated daily. This algorithm considers that EWMA is an efficient algorithm observing long-term seasonal conditions with no mechanism for adapting to relatively short-term (hourly or daily) variations.

SEP-DR takes advantage of the properties of EWMA in that a feature acquiring the status of the current solar condition is employed. To do this, SEP-DR updates equation (3.4) with a new parameter, called the daily ratio (퐷푟푎푡푖표), as presented in equation (3.7).

퐸(푑, 푛 + 1) = 퐸퐸푊푀퐴(푑, 푛 + 1). (1 + 퐷푟푎푡푖표) (3.7)

This ratio reflects the progress behavior of solar energy generation in the most recent timeslots within the current day. Thus, 퐷푟푎푡푖표 can be determined as increasing/ decreasing ratio as well as positive / negative ratio. However, a daily ratio 퐷푟푎푡푖표 can be considered to the average of increments / decrements over a specified number of previous timeslots 퐾 in the current day as in (3.8) [99]. A graphical representation for this algorithm is shown in Fig. 3.5.

∑퐾 (퐸(푑,푛−푗)−퐸(푑,푛−푗−1)) 퐷 = 푗=0 (3.8) 푟푎푡푖표 퐾

1 − 훼

+ 훼

×

+ 1

퐷푟푎푡푖표

Fig.3. 5. Graphical representation of SEP-DR algorithm

Statistical prediction algorithms 40

3.4.1.4 SEPAD

This algorithm refers to the Solar Energy Predication based on Additive Decomposition (SEPAD). This algorithm identifies the most prominent terms in solar harvested energy prediction and considers their behavior separately. After that, all these terms are combined together to find the predicted energy. Similar to the basic statistical algorithms, the day is divided into a timeslots. For each of these slots, the three terms can be calculated as in (3.9):

퐸(푑, 푛 + 1) = 퐸푐푝(푑, 푛 + 1) + 퐸푠푝(푑, 푛 + 1) + 퐸푑푝(푑, 푛 + 1) (3.9) where,

퐸푐푝(푑, 푛 + 1) : Energy predicted according to solar diurnal cycle. 퐸푠푝(푑, 푛 + 1) : Energy predicted according to seasonal cycle. 퐸푑푝(푑, 푛 + 1) : Energy predicted according to current daily trend.

This algorithms implements three EWMA filters to predict aforementioned terms. Thus, three weighting parameters are used. This algorithm has an advantage of flexible independent tuning of different terms for different data trends. However, the cyclic energy prediction is performed by considering past days history as in EWMA. It also assigns higher weight to the most recent past sample. Consequently, 퐸푐푝(푑, 푛 + 1) can be expressed as in (3.10).

퐸푐푝(푑, 푛 + 1) = 훼 퐻(푑, 푛) + (1 − 훼) 퐻(푑, 푛 − 1) (3.10)

In order to integrate the seasonal variations, the difference between the actual harvested energies in the past days for the same slot. If there is no difference, no seasonal effect will assumed. Otherwise, a tuning parameter 훽 is used to scale these changes as shown in following equation:

퐸푠푝(푑, 푛 + 1) = 훽[퐻(푑 − 1, 푛 + 1) − 퐻(푑 − 2, 푛 + 1)] +(1 − 훽)[퐻(푑 − 2, 푛 + 1) − 퐻(푑 − 3, 푛 + 1)] (3.11)

An initial prediction for solar harvested energy 퐸푖푝 is calculated by adding energy predicted due to diurnal cycle 퐸푐푝(푑, 푛 + 1) and energy predicted due to seasonal cycle 퐸푠푝(푑, 푛 + 1) as in (3.12). If solar energy is consistent and follows past day’s patterns then this guess is very close to the actual one.

퐸푖푝(푑, 푛 + 1) = 퐸푐푝(푑, 푛 + 1) + 퐸푠푝(푑, 푛 + 1) (3.12)

State of the art for solar energy prediction in wireless sensor nodes 41

Moreover, considering the latest trend in the current day is also important because a cloudy day or a rainy day can come out after sunny days or even rain can start in afternoon after sunny morning. Thus, in order to know recent daily trend, the initial predicted energy is subtracted from the actual harvested energy of corresponding slots.

퐸푑푝(푑, 푛 + 1) = 훾[퐻(푑, 푛) − 퐸푖푝(푑, 푛)] + (1 − 훾)[퐻(푑, 푛 − 1) − 퐸푖푝(푑, 푛 − 1)] (3.13) where 훾 is tuning parameter. Lastly, initial energy predicted is added to energy predicted according to current daily trend in order to find a complete prediction as in (3.14) [73].

퐸(푑, 푛 + 1) = 퐸푖푝(푑, 푛 + 1) + 퐸푑푝(푑, 푛 + 1) (3.14)

3.4.2 Weather conditioned algorithms

This category includes six prediction algorithms. One of them is basic and the others are enhanced algorithms of it.

3.4.2.1 WCMA

Weather Conditioned Moving Average (WCMA) algorithm handles the deficiencies of the previous EWMA algorithm. Thus, it is an improved version of it. It considers the harvested or recorded energy readings for both, current and past days. These readings are stored in a matrix 퐻(푖, 푗), where 푗 is a sample of measured energy at 푖푡ℎ day. In contrast to EWMA algorithm, WCMA incorporates the energy harvested in the previous time slot into the prediction equation instead of using only readings of past days. Additionally, the average of harvested energy readings contributes to the prediction equation which is expressed in (3.15).

퐸(푑, 푛 + 1) = 훼 퐻(푑, 푛) + (1 − 훼) 푀퐷(푑, 푛 + 1). 퐺퐴푃 (3.15)

In the previous equation, 푀퐷(푑, 푛 + 1) represents the mean value of the previous samples. The average value of the 푛푡ℎ sample on the 푑푡ℎ day is calculated for 퐷 past days as in (3.16)

1 1 푀 (푑, 푛) = ∑푑−퐷 퐻(푖, 푛) = ∑퐷 퐻(푑 − 푖, 푛) (3.16) 퐷 퐷 푖=푑−1 퐷 푖=1

Statistical prediction algorithms 42

Additionally, a new factor called 퐺퐴푃 is used to reflect the current day solar condition relative to the past days. The 퐺퐴푃 factor is computed for past 퐾 samples. So, a vector 푉 of size 푘 is created to indicate each value of the past 퐾 samples (3.17). Each sample denotes the ratio of the harvested energy to the mean value (3.18).

푉 = [푣1, 푣2, 푣3, … . . , 푣푘] (3.17)

퐻(푑,푛−퐾+푘) 푣푘 = (3.18) 푀퐷(푑,푛−퐾+푘)

Once the elements of the vector 푉 are calculated, these values are weighted according to their distance from the actual sample. This is to give more importance to the closer samples and less importance to the far samples. To do this, a vector 푃, is defined as in (3.19). Each element in vector 푃 is actually inversely proportional to the distance from the current value 푝푘 (3.20) Thus, 퐺퐴푃 value is finally calculated as in (3.21) [100].

푃 = [푝1, 푝2, 푝3, … . . , 푝푘] (3.19)

푘 푝 = (3.20) 푘 퐾

푉.푃 푣 푝 +푣 푝 +푣 푝 +⋯+푣 푝 퐺퐴푃 = = 1 1 2 2 3 3 푘 푘 (3.21) ∑ 푃 푝1+푝2+푝3+⋯+푝푘

The graphical representation of WCMA algorithm is shown in Fig. 3.6 [101].

훼

+ 1 − 훼

× 퐺퐴푃

Fig.3. 6. Graphical representation of WCMA algorithm

State of the art for solar energy prediction in wireless sensor nodes 43

Although that this algorithm behaves better than EWMA, high prediction errors still appear at sunrise and sunset periods. This due to neglecting the one of the two terms in equation (3.15) because of zero values are registered for 퐻(푑, 푛) at sunrise and for 퐺퐴푃 at sunset time.

3.4.2.2 WCMA-PDR

This algorithm presents an improvement of the first version “WCMA”. The aforementioned high prediction errors that occur at the sunrise and sunset are treated and reduced in this algorithm. These errors are due to a prediction delay called “Phase Displacement” which are caused mainly by two quantities, 훼 and the last registered harvested energy 퐻(푑, 푛).

The idea is to reduce this error by a feedback response that takes into account the resulted error in the past predictions. This new algorithm is called Weather Conditioned Moving Average - Phase Displacement Regulator (WCMA-PDR). This algorithm creates a vector called Prediction Error “푃퐸” vector of length 푁 stores in memory an error value between the measured readings and the corresponding predicted ones by WCMA algorithm. The elements of this vector are calculated by (3.22).

푃퐸(푑, 푗) = 휂 푃퐸(푑 − 1, 푗) + (1 − 휂) . 푃푟푒푑 퐸푟푟표푟(푑, 푥) (3.22) where, 푃퐸(푑, 푗) : is the value of the vector 푃퐸 for the 푗푡ℎ time slot and the 푑푡ℎ day. 휂 : is a weighting factor.

Since the vector “푃퐸” contains a memory of the error presented in the past days, summing the correspondent value of the vector to the prediction can provide double effect:  If the sunrise and sunset times do not have high changes in consecutive days, the sum can erase or highly decrease the error in the prediction.

 If, on the contrary, there is a displacement between the two values, the error in the prediction will be doubled and the benefit of the vector 푃퐸 is cancelled.

To limit this problem, the direct summation is avoided through creating a new value derived from the correspondent value attenuated by its closest values in the vector. The new rule introduced depends on two new factors. Factor (휗) indicates the weight of any value compared to the less significant values and the (푊푝표푟) factor indicates the width of the portion of “푃퐸” vector that affects the calculation of the new value to be

Statistical prediction algorithms 44 summed to the prediction. Consequently, the phase displacement can be calculated by the next equation:

푊푝표푟−1 (푊 +1) 푝표푟푖 ∑푊 =0[푃퐸((푛−푊푝표푟 )푚표푑(푁))+푃퐸((푛+푊푝표푟 )푚표푑(푁))] .휗 푝표푟푖 푖 푖 푃퐷푅(푑, 푛) = [ 푊푝표푟−1 (푊 +1) ] . 퐴푑 (3.23) 푝표푟푖 ∑푊 =0 휗 푝표푟푖

The value of 푃퐷푅(푑, 푛) that is added to the prediction derives from the sum of the value 푃퐸(푑, 푛) and (푊푝표푟 − 1) values previous and consecutive .The weight given to these values depends on the distance from the time slot 푗 to predict. An attenuation factor (퐴푑) is also added to regulate the effect of the phase displacement regulator.

After defining the equations for the upgrade of the algorithm WCMA, an optimization for the whole parameters presented in terms of error produced as it is being presented in the next section. The value 푃퐷푅(푑, 푛) is added to the main equation of the WCMA algorithm in three different ways defined in Equations (3.24 to 3.26) and its effect is evaluated [102].

푃퐷푅 퐸1 (푑, 푛 + 1) = 훼 퐻(푑, 푛) + (1 − 훼) 푀퐷(푑, 푛 + 1). 퐺퐴푃 + 푃퐷푅(푑, 푛 + 1) (3.24)

푃퐷푅 퐸2 (푑, 푛 + 1) = 훼 퐻(푑, 푛) + (1 − 훼)[푀퐷(푑, 푛 + 1). 퐺퐴푃 + 푃퐷푅(푑, 푛 + 1)] (3.25)

푃퐷푅 퐸3 (푑, 푛 + 1) = 훼 퐻(푑, 푛) + (1 − 훼)[퐺퐴푃(푀퐷(푑, 푛 + 1). +푃퐷푅(푑, 푛 + 1))] (3.26)

3.4.2.3 WCSMA

In this section, Weather Conditioned Selective Moving Average (WCSMA) algorithm is presented. EWMA is the foundation of this algorithm. WCSMA considers the seasonal changes by adapting both the variations in the time of sunrise and sunset, as well as the difference in solar power between seasons. Also, the algorithm attempts to achieve more accurate prediction.

WCSMA improves the performance through two main strategies. One is classifying the past days according to their weather conditions, the other is using the Weighted- Average based on similarity. The method achieves high accuracy especially when similar trend is provided in the past days.

The predicted value is related to the previous slot in the same day and the selective moving average of the past samples for the same slot in the past days. Therefore, the new prediction algorithm as follows:

State of the art for solar energy prediction in wireless sensor nodes 45

퐸(푑, 푛 + 1) = 훼 퐻(푑, 푛) + (1 − 훼) 퐻(푖, 푛 + 1) (3.27)

Practically, the energy obtained during cloudy days is less than half of that collected during sunny days. Thus, a cloudy day will bring more prediction error of a day which is actually sunny. In order to reduce this kind of impact, we store the harvested solar energy within a day into sunny, cloudy or mix matrixes respectively according on amount of received energy. And it should be guaranteed that only the energy data in the past sunny days will be used for the prediction of a current sunny day.

There are three energy matrices which are 퐻푠푢푛푛푦, 퐻푐푙표푢푑푦 and 퐻푚푖푥 in this algorithm. The size of each matrix is 퐷 × 푁. The following equations are used to get average obtained energy in past days with same weather condition.

푖=퐷 푁 (∑ ∑ 퐻푠푢푛푛푦(푖,푗)) 퐻̅ = 푖=1 푗=1 (3.28) 푠푢푛푛푦 퐷 푖=퐷 푁 (∑ ∑ 퐻푐푙표푢푑푦(푖,푗)) 퐻̅ = 푖=1 푗=1 (3.29) 푐푙표푢푑푦 퐷 푖=퐷 푁 (∑ ∑ 퐻푚푖푥(푖,푗)) 퐻̅ = 푖=1 푗=1 (3.30) 푚푖푥 퐷

By comparing the available overall energy in current day and the average energy of three energy matrixes in the previous 퐷 days, the three energy matrixes are updated every day.

Considering among sunny days or cloudy days, we utilize the models of energy harvesting tendency of past days to estimate the energy available in the subsequent days. The following equation calculates the similarity-weighted average of energy harvested in past 퐷 days at slot 푛 + 1 :

퐷 퐻(푖, 푛 + 1) = ∑푖=1 푥푖 . 퐻푚(푖, 푛 + 1) (3.31)

Where, 푥푖 is weighting coefficient, 퐻푚denotes one of three energy matrixes.

For calculating 푥푖, a parameter called 푆퐼푀푖 is calculated based on the principle of “Euclidean distance” as in Equation (3.32). It is taken as the weight of two days. Larger 푡ℎ 푆퐼푀푖 denotes higher similarity between two energy sequences of 푖 day and current day.

1 푆퐼푀푖 = (3.32) 1 + 푆퐹.푑푖푠푖(퐾)

Statistical prediction algorithms 46

where 푆퐹 is a scaling factor associated to the steepness of the 푑푖푠푖(퐾). The default scaling is (푆퐹 = 1). The other factor 푑푖푠푖(퐾) is calculated using Equation (3.33) denotes the distance between the energy sequence of 푖푡ℎ day and current day in 퐾 slots:

푛 2 푑푖푠푖(퐾) = √∑푗=푛−퐾(퐻(푖, 푗) − 퐻(푑, 푗)) (3.33)

Comparing the similarity of past 퐾 slots between current day and past days in three energy matrices respectively and find the matrix that the day has highest similarity belongs to. For example, if the one in sunny matrix has highest similarity with present day, sunny matrix, 퐻푠푢푛푛푦 will be used as 퐻푚.

퐻푠푢푛푛푦 퐻(퐾, 퐽) ∈ 퐻푠푢푛푛푦

퐻푚 = {퐻푐푙표푢푑푦 퐻(퐾, 퐽) ∈ 퐻푐푙표푢푑푦 (3.34) 퐻푚푖푥 퐻(퐾, 퐽) ∈ 퐻푚푖푥

After 퐻푚 is determined, the weighting coefficient 푥푖 is used to describe proportion t 푡ℎ occupied by the 푖 day in 퐻푚 in prediction. 푥푖 is calculated as follows:

푆퐼푀푖 푥푖 = 퐷 (3.35) ∑퐾=1 푆퐼푀푘

Where, 푆퐼푀푘 = 푆퐼푀푚푎푥, 푛 − 퐾 < 푗 < 푛

The weighting coefficient 푥푖 plays an important role in improving the accuracy of prediction. When there exist some trend models in 퐻푚 matching the current day well, 푥푖 gives the model higher weighting in prediction which lead to more precise prediction. On the contrary, when there exists no exact matched model samples,

퐻(푑, 푛 + 1) will close to average value of corresponding values in 퐻푚, which can provide less error [103].

3.4.2.4 AR-WCMA

This algorithm indicates to Auto Regressive Weather Conditioned Moving Average (AR-WCMA). The key idea is to apply autoregressive time series model to the usual WCMA algorithm. This is attributed to the fact of the periodic behavior of the daily solar radiation. Thus, this model can improve prediction resulted from WCMA [104].

The auto regression process model is used to fit discrete measured energy values to a set of linear coefficients from past observations in the current day as next:

State of the art for solar energy prediction in wireless sensor nodes 47

̃ 푘 퐻(푑, 푛 + 1) = ∑푖=1 푎푖 . 퐻(푑, 푛 + 1 − 푖) + 푟푘 (3.36) where,

푎푖 Coefficients which are calculated daily based on data from 퐷 past days. 푟푘 Residual constant term. In order to further improve the resulted predicted values, it will be integrated to the normal predicting equation (3.15) of WCMA as next:

퐸(푑, 푛 + 1) = 훼 퐻̃(푑, 푛 + 1) + (1 − 훼) 푀퐷(푑, 푛 + 1). 퐺퐴푃 (3.37)

3.4.2.5 D-WCMA

This algorithm indicates to Dynamic Weather Condition Moving Average (D-WCMA). This algorithm adopts the idea of utilizing a dynamic weighting factor instead of fixed one (훼) as in EWMA, normal WCMA and Pro-Energy. The prior selecting of a fixed weighting factor does not ensure that these algorithms will adapt well to the sudden variations of the weather conditions [74].

This algorithm modifies WCMA algorithm using a gain called 퐺1. This gain works as a time varying weighting parameter. Thus, it is used as alternative of (훼). This parameter is adapted according to the variations in the original profiles of energy stored in the matrix. Then, the predicted energy is obtained by:

퐸(푑, 푛 + 1) = 퐺1(푑, 푛 + 1) . 퐻(푑, 푛) + (1 − 퐺1(푑, 푛 + 1)). 푀퐷(푑, 푛 + 1). 퐺퐴푃 (3.38) where, 1 휎(푑,푛+1) 퐺1(푑, 푛 + 1) = × (3.39) 2 휎(푑,푛+1)+ 휎1(푑,푛+1)

In this Equation, 휎(푑, 푛 + 1) refers to the standard deviation of the stored energy profiles at time 푛 + 1 with respect to the mean value. And it is calculated by:

1 2 휎(푑, 푛 + 1) = √ ∑푑 ((푥 (푑, 푛 + 1)) − (푀 (푑, 푛 + 1))) (3.40) 푑 푖=1 푖 퐷

Also, 휎1(푑, 푛 + 1) is the standard deviation characterizing the energy variations in the stored profiles between the time slots 푛 and 푛 + 1. And it is calculated by:

2 1 휎 (푑, 푛 + 1) = √ ∑푑 ((∆ (푑, 푛 + 1)) − (푀 (푑, 푛 + 1))) (3.41) 1 푑 푖=1 1푖 1 where,

Statistical prediction algorithms 48

( ) ( ) ( ) ∆1푖 푑, 푛 + 1 = 푥푖 푑, 푛 + 1 − 푥푖 푑, 푛 (3.42) and 1 푀 (푑, 푛 + 1) = ∑푑 ∆ (푑, 푛 + 1) (3.43) 1 푑 푖=1 1푖

3.4.2.6 UD-WCMA

This algorithm refers to Universal Dynamic Weather Condition Moving Average (UD- WCMA). It is improved version of (D-WCMA) algorithm. The improvement has been done by replacing the last observation 퐻(푑, 푛) with a weighted linear combination of the last observation 퐻(푑, 푛) and the closest energy pattern in memory denoted ∗ by 푥푖 (푑, 푛 + 1) [74].

The linear combination is weighted by an adaptive factor 퐺(푑, 푛 + 1) depending on the variations of the current day measurements, as follows:

· 푥̂(푑, 푛 + 1) = 퐺1(푑, 푛 + 1)[퐺(푑, 푛 + 1)퐻(푑, 푛) + (1 − 퐺(푑, 푛 + 1))푥푖(푑, 푛 + 1)] +1 − 퐺1(푑, 푛 + 1) 푀퐷(푑, 푛 + 1) (3.44) where,

퐺(푑, 푛 + 1) = 퐺1(푑, 푛 + 1) + 퐺2(푑, 푛 + 1) (3.45) and 1 휎(푑,푛+1) 퐺2(푑, 푛 + 1) = × (3.46) 2 휎(푑,푛+1)+ 휎2(푑,푛+1)

In this equation, 휎2(푑, 푛 + 1) denotes to the standard deviation of the variations in the solar energy vector 퐻 between consecutive time slots along a window of size 퐾.

Precisely, the vector of consecutive variations defined by ∆2(푑, 푛 + 1) is given by:

( ) ( ) ( ) ∆2푘 푑, 푛 + 1 = 퐻 푛 + 1 − 푘 − 퐻 푛 − 푘 (3.47) where 푘 = 1, … . 퐾 − 1 The corresponding mean and standard deviation are defined by:

1 푀 (푑, 푛 + 1) = ∑퐾−1 ∆ (푑, 푛 + 1) (3.48) 2 퐾−1 푘=1 2푘 and 1 휎 (푑, 푛 + 1) = √ ∑퐾−1(∆ (푑, 푛 + 1) − 푀 (푑, 푛 + 1)) (3.49) 2 퐾−1 푘=1 2푘 2

State of the art for solar energy prediction in wireless sensor nodes 49

3.4.3 Profile energy algorithms

This category includes three prediction algorithms. One of them is basic and the others are improved versions of it.

3.4.3.1 Pro-Energy

Pro-Energy algorithm, like WCMA, considers energy profiles of past and current days for predicting energy income for upcoming time slots. A matrix 퐻(푖, 푗) is derived to record the harvested energy of 퐷 past days for all considered time slots. The energy which observed during the current day is stored in a vector 퐶. The graphical representation is shown in Fig. 3.7 [105].

퐻

푑 퐻

1 − 훼

퐶 +

훼

Fig.3. 7. Graphical representation of Pro-Energy algorithm

The main idea of Pro-Energy algorithm is to extract an energy profile from the matrix 퐻(푖, 푗). The extracted profile (퐻푑 ), must be taken at a time slot corresponds to 푑 the predictable one. So, it is denoted as 퐻푛+1 . Additionally, it must extracted from the most similar day to the current one. The referred extracted profile in addition to the energy profile in the current day prior the predictable slot, 퐶(푑, 푛) will predict the amount of energy at time slot (푛 + 1) as in (3.50).

푑 퐸(푑, 푛 + 1) = 훼 퐶(푑, 푛) + (1 − 훼) 퐻푛+1 (3.50)

Statistical prediction algorithms 50

Calculating of Mean Absolute Error (푀퐴퐸) is the mathematical adapted procedure to extract the desired profile from the most similar day (3.51). The day of the lowest 푀퐴퐸 is the most similar day (3.52).

1 푀퐴퐸 = ∑푛 |퐶 − 퐻푑 | (3.51) 퐾 푖=푛−퐾+1 푖 푖

1 퐻푑 = Min ∑푛 |퐶 − 퐻푑 | (3.52) 푛+1 푑 푖=푛−퐾+1 퐾 푖 푖 퐻푛+1 ∈퐸

In order to enhance the performance of this algorithm, the most similar day can be extracted from a matrices of typical days. This means that the successive days may be classified firstly into sunny, cloudy or rainy days and then a characteristic profile may be associated to each of these categories [105].

3.4.3.2 IPro-Energy

This algorithm is denoted by Improved Pro-Energy algorithm (IPro-Energy). The improvement is performed through a technique that allows to combine multiple profiles instead of revising them as in the basic Pro-Energy. This algorithm selects a set of most similar profiles stored in 퐻 matrix for the 퐷 past days and combines them to form a Weighted Profile (푊푃). For instance, when a sunny timeslot is followed by a cloudy or rainy one, considering only a single profile may lead to poor accuracy. On the contrary, considering multiple profiles allows to account for these potential changes, reducing the prediction error [91].

The logical explanation behind using of multiple is to consider different possible states for the predictable timeslot. This algorithm has two main features. It does not need to classify the typical days according to their characteristics as in WCSMA.

푑1 푑2 푑푝 Assuming that 퐻푛+1 , 퐻푛+1 , ..., 퐻푛+1 are the most similar profiles to the current day 퐶, The weighted profile (푊푃) for the predictable harvested time slot 푛 + 1 is calculated by:

1 푊푃 = ∑푃 (휔 . 퐻푑푗 ) (3.53) 푛+푖 푃−1 푗=1 푗 푛+푖 where, 푑푗 푀퐴퐸푘(퐻 ,퐶) 휔푗 = 1 − 푃 푑푗 (3.54) ∑푗=1 푀퐴퐸푘(퐻 ,퐶) Consequently, the predicted energy is then computed as in the next:

퐸(푑, 푛 + 1) = 훼 퐶(푑, 푛) + (1 − 훼). 푊푃푛+1 (3.55)

State of the art for solar energy prediction in wireless sensor nodes 51

3.4.3.3 Pro-Energy-VLT

Pro-Energy-VLT is referred to Profile Energy algorithm with Variable-Length Timeslots. This algorithm adds an improvement of the Pro-Energy algorithm. The principle of improvement is that the prediction of the captured energy can be better reached by using timeslots with different durations. So that energy predictions are updated more or less frequently based on how rapidly the current energy harvesting rate is changing.

Pro-Energy-VLT dynamically resizes the prediction timeslots so as to obtain a timeslots divisions that is rough (during periods of low dynamicity) or smooth (during periods of high dynamicity) based on the dynamics of the power source. Pro-Energy-VLT works as follows.

1) During the initial setup phase, each day is divided into 푁 equal-length timeslots, as in Pro-Energy.

2) A weight is then assigned to each timeslot 푛, based on the variability of the harvesting process during timeslot 푛 with respect to the previous timeslot 푛 − 1. Thus, the resulted weights measure how rapidly the availability of the energy source changes over different timeslots. These weights can be calculated according to:

휔푛 = 푙표푔(푑푛|푝푛 + 푝푛−1| + 1) (3.56) where,

푑푛 : is the duration of timeslot n. 푝푛 and 푝푛−1 : are the average power harvested during timeslots 푛 and 푛 − 1.

This adapting phase for the weights runs periodically by each node. Every 퐷 days, Pro-Energy-VLT uses the information collected over the past 퐷 days to produce a new timeslot setting, in which the 푁 timeslots used for prediction are re-distributed based on the dynamics of the energy source.

The higher weights are assigned to timeslots that cover periods of time in which the availability of the solar energy varies sharply. On contrast, lower weights are associated with stable levels of solar energy.

3) Pro-Energy-VLT redistributes the 푁 timeslots based on their weights. In this process, the adjacent timeslots having a weight equal to zero or less than a threshold value are identified and merged together. For example, in the solar energy harvesting case, a single large timeslot is created to cover the whole

Artificial Intelligence (AI) prediction algorithms 52

night, by merging 푧 timeslots together. The remaining 푁 − 푧 timeslots are then distributed proportionally to their weights. In particular, each timeslot 푛 is split

into a number 푛푛푒푤 of sub-timeslots, as follows:

휔푛 푛푛푒푤 = [ . (푁 − 푧)] (3.57) ∑푛 휔푛

4) As a last step, the pool of stored profiles maintained by Pro-Energy-VLT is updated to reflect the new timeslot setting. To this end, the energy harvesting profiles stored in the pool are interpolated to determine the power values corresponding to the new timeslots setting [106].

3.5 Artificial Intelligence (AI) prediction algorithms

The algorithms of artificial intelligence are the most recent prediction algorithms at all. These algorithms are also applicable in other technical fields where the prediction is needed. The most common algorithms are fuzzy logic and neural networks. The basic principle of these approaches is to train the machine on a real measured data. Then, the machine will be able to perform the prediction by itself.

The accuracy of these methods differs according to the data used in the training stage. Thus, in some cases the statistical algorithms as well as the stochastic ones can show an accuracy performance greater than AI algorithms. In general, when the size, type and the number of input parameters considered in the training phase increased, more accuracy will obtained.

3.5.1 Fuzzy Logic

The fuzzy logic is a mathematical adjustment/correction tool to express the human thinking. This algorithm allows to measure the ambiguous or uncertainty which is indicated by a description words like small, large, low, high, medium …etc, within a set called fuzzy set. This set includes a rules that describe the behavior of output variable with respect to the behavior of different input variables. However, for both input and output variables, a membership function is created based on a predefined measured data. The membership function is describing the variation of the membership degree (weight) on Y- axis with respect to a real values for the variables on X- axis as in Fig.3.8 [31,107].

State of the art for solar energy prediction in wireless sensor nodes 53

low medium high very high 1

Membership Degree Membership 0 푥 푥 푥 푥 1 2 3 4 Input Variable Fig.3. 8. Fuzzy set for an input variable

3.5.2 Neural Networks

Artificial Neural Network (ANN) is a machine learning methodology [108]. This means that it works based on a set of predefined historical data. The network is trained on premeasured data to give a result about the desired output (퐺푆푅). In other words, artificial neural network is a modelling technique of linear and non-linear functions. Basically, the neural network is structured of three layers (input, hidden and output). The basic element of each layer is called neuron and each layer consists of number of neurons (Fig. 3.9) [109]. Each neuron has a specific bias and is connected with the other neurons in the adjacent layers by edges that have a certain weights.

Hidden Input

Output

Neuron

Fig.3. 9. Structure of artificial neural network

Artificial Intelligence (AI) prediction algorithms 54

The processing of neural network divides into two actions, learning (training) and normalization (generalization). These processes can be implemented in the following sequence in order to predict (퐺푆푅) values [109–117]:

(i) The first step is to normalize the premeasured input and output values. This is performed by dividing these values over the maximum value for each of them.

(ii) The second step is to define the size of the input matrix (i.e. number of input parameters and number of samples available).

(iii) Creating of the neural network by selecting the number of hidden layers, number of neurons in each layer and the activation function.

(iv) Training the neural network: During training, a signals are calculated for each neuron in the first layer using inputs, weights and the biases according to (3.58). These signals are applied to predefined activation function. The result of the activation function is passed to the adjacent neurons in the next layer as input. This process continues until the last output layer.

푆푗 = ∑푖=1(푋푖. 푤푖푗 ) + 푏푗 (3.58) where,

푆푗 : Signal at neuron 푗.

푋푖 : Input at neuron 푖. 푤푖푗 : Weight of connection between neuron 푖 and neuron 푗. 푏푗 : Bias of neuron 푗.

(v) Minimizing the error: the calculation process is performed iteratively to minimize the network performance function which is described by Mean Square Error (MSE). In each iteration, the values of weights and biases were updated. (vi) Generating an output matrix with a size similar to the input matrix. (vii) As a last step, the output values are un-normalized and their accuracy are assessed by correlating the predicted output values with measured ones. This is implemented through calculating the correlation coefficient (푅) as in (3.59).

푇 ∑푡=푇 푥 .푦 −(∑푡=푇 푥 )(∑푡=푇 푦 ) 푅 = 푡=1 푡 푡 푡=1 푡 푡=1 푡 (3.59) 푡=푇 2 푡=푇 2 푡=푇 2 푡=푇 2 √[푇 ∑푡=1 푥푡 −(∑푡=1 푥푡) ].[푇 ∑푡=1 푦푡 −(∑푡=1 푦푡) ]

State of the art for solar energy prediction in wireless sensor nodes 55

where,

푥푡 : Measured 퐺푆푅 at specific hour t. 푦푡 : Predicted 퐺푆푅 at specific hour t. 푇 : Total number of hourly samples of 퐺푆푅.

The activation function that can be used within the artificial neural network is variant (Table 3.1). Consequently, many models have been created by changing the number of neurons, hidden layers, activation functions as well as the number and kind of input parameters. The most common one called feed forward pack propagation network. These are networks in which signals flow from the input to the output neurons in forward direction. It is also includes a closed loop for propagation [118–120].

Table 3. 1. Activation functions that are used with neural networks

Activation Function Function function expression range Linear 푥 −∞ < 푥 < ∞

Logistic (Logsig) 1 0 ≤ 푥 ≤ +1

1 + 푒−푥 푥 −푥 Hyperbolic (Tansig) 푒 − 푒 −1 ≤ 푥 ≤ +1

푒푥 + 푒−푥 Exponential 푒−푥 0 ≤ 푥 < ∞

3.6 Performance comparison of prediction algorithms

For the whole aforementioned prediction algorithms, the performance can be measured in terms of three main points / indices: the prediction accuracy which is usually indicated or expressed by the maximum prediction error occurred, the execution time and the reserved space in the memory. As a principle, the optimal algorithm should fulfil highest prediction accuracy (i.e. lowest prediction error), lowest execution time and lowest reserved space in the memory.

Table 3.2 shows a recorded values for these performance indices for all aforementioned prediction algorithms at one hour prediction horizon (i.e. 24 predicted values per a day). From the table, the stochastic algorithms represented by ASIM algorithm has the highest maximum prediction error with little execution time. For the statistical

Performance comparison of prediction algorithms 56 algorithms, the performance indices were variant. For example, the maximum prediction error may reach 40% in EWMA algorithm and 7% in Pro-Energy-VLT. The other statistical algorithms record a maximum prediction error between these two values. Also, the execution time for EWMA reaches 0.01 second, while it is 0.392 second for Pro-Energy-VLT.

The AI algorithms, Like Fuzzy Logic and Neural Networks, show the lowest value of the maximum perdition error compared to the others. This value comes in within a range of 6-9% for Fuzzy Logic and 5-7% for the Neural Networks algorithm. Although that this is considered an advantage regarding the most important index (Prediction accuracy), they need larger space in the memory which is finally an additional cost. In addition to that, increasing the execution time leads to consume more power. However, these algorithms can be utilized according to the requirements of the application, the most critical index with respect to the user and the ability of the manufacturers.

Table 3. 2. Performance comparison of different prediction algorithms [73,74,91,92,97– 104]

Performance index Prediction Maximum prediction Execution Reserved space algorithm error occurred time in the memory in (%) in (sec) in (byte) ASIM 30-70 0.013 228 EWMA 28-40 0.010 96 ASEA 25-33 0.028 106 SEP-DR 22-30 0.032 116 SEPAD 20-28 0.045 132 WCMA 18-25 0.051 384 WCMA-PDR 16-24 0.083 480 WCSMA 14-22 0.088 580 AR-WCMA 13-21 0.068 418 D-WCMA 12-20 0.094 450 UD-WCMA 10-18 0.098 490 Pro-Energy 10-16 0.160 488 IPro-Energy 9-14 0.281 523 Pro-Energy-VLT 7-12 0.392 617 Fuzzy Logic 6-9 0.630 633 Neural Networks 5-7 0.711 520

State of the art for solar energy prediction in wireless sensor nodes 57

3.7 Adopted methodology

As the neural network is the most recent algorithm utilized for the prediction of solar energy in wireless sensor nodes, this thesis will focus on it and attempt to improve it more and more. This improvement would add another achievement in the field of solar energy prediction in that nodes.

The traditional application of the neural network which use the recent observations of energy as input parameters (during the training phase) will convert into an application of another type. In this regards, a meteorological input parameters will be considered. And because the prediction accuracy is considered the most highlighted performance index, if these parameters does not fulfill an acceptable level of accuracy, it will be enhanced with a results from a statistical algorithms as additional input parameters. The next chapter will handle this contribution in details.

Accuracy improvement of an adapted predictive neural network 59

4 Accuracy improvement of an adapted predictive

neural network

This chapter shows in Sec. 4.1 the challenges of the predictive neural networks in wireless sensor nodes. In Sec. 4.2, an objectives of neural network based prediction are mentioned. Sec. 4 .3 handles different neural networks that use a meteorological input parameters for calculating solar energy. Sec. 4 .4 describes the process of adapting a predictive neural networks of meteorological parameters to be used with sensor nodes. This includes identifying the suitable meteorological input parameters as well as the suitable topology. Sec. 4.5 shows the data base which is used for input and output parameters during the analysis. Both, Sec. 4 .6 and Sec. 4.7 are devoted to describe a model for the zenith angle as a meteorological input parameter of the neural network utilized with sensor node. This basically focus on a mathematical expression, its variables and its layout. In Sec. 4.8, an evaluation for the adapted predictive neural network is performed and compare to three main statistical algorithms and the traditional networks. Sec. 4.9 proposes an accuracy improvement for the adapted predictive neural network by adding statistical algorithm. This section is accompanying with an evaluation to the perdition accuracy.

4.1 Challenges of predictive neural networks in wireless sensor nodes

Due the special features of the sensor node and low power systems, a necessary requirements arise with the neural networks algorithm. In general, the neural network should fulfill the following aspects to be used with sensor nodes:

 Executability on microcontroller: Most of the microcontrollers are designed to implement a simple processing for the data i.e. a processing of low computational efforts. Thus, the computational resources to run or execute the neural networks are limited. This is attributed to existing thousands of iterations on a training data which require long time exceeds the prediction horizon. Consequently, the neural networks can be

Challenges of predictive neural networks in wireless sensor nodes 60

implemented in a smart way through simulating the network in an outside environment i.e. performing the training process using a fast machines like computers and then copying the weights and biases to the microcontroller’s board to be used within a simple equations. This is what is known by “off line /off board training”.

 The prediction in advance: The sensor nodes are designed to be autonomous systems. Thus, the prediction in these nodes should be performed in advance by supplying them with a predefined values for the input parameters. This basically refers to the ability to draw a model / curve for the utilized input parameters in the microcontroller. Using an input parameters that cannot be modeled require a measuring devices which record a measured values at the current state and not at the future horizon of prediction. For this reason, a model is needed to express a fixed pattern for variations of input parameters.

 Simplicity of the topology: The memory available in the low power systems is determined by the microcontroller. The typical sizes for the utilized memory are 2, 4, 8 Kilobyte. It should be noted that the degree of complexity of the microcontroller correlates with the available memory. More complex network’s topology means more weights, biases and equations to use. Thus, longer program code is required. Simplifying topology here leads to the lower number of neurons, layers and input parameters. The simple topology in its role reduces the number of utilized weights and biases in order to make the network executable on the memory.

 The functional and energy balance: The neural network should allow a safer function and an improved planning of the operating conditions. It is important that the additional functionality creates added value for the system. If the implementation consumes significantly more energy than without it or if the increase in functionality is disproportionate to the additional consumption, the solution is to be regarded as unusable.

 The small size of the node: This basically indicates to the necessity of using input parameters without measuring devices. The larger measuring units the larger sensor node. Using more measuring units has also negative effect on the size.

 High level of accuracy: The total error between the predicted value and actual one should be minimal. This enhance the management of energy consumption in the nodes.

Accuracy improvement of an adapted predictive neural network 61

4.2 Objectives of predictive neural networks in wireless sensor nodes

For the control of the operating states of a wireless sensor node through energy management scheme, a prior knowledge about the harvestable energy is required. Due to the challenge of advance prediction that characterize the wireless sensor systems (Sec. 4.1), the neural networks that can be used with these systems have a specific input parameters. Simultaneously, the prediction using a traditional neural networks that use the past harvested energy observations do not meet the purpose and contribute to more energy consumption or even less reliability. Therefore, the main objective is to adapt a neural network with a meteorological input parameters which allow an advance prediction from one side and fulfill a performance exceeds in its accuracy the performance by the traditional neural networks from other side. The working algorithm to fulfill this target is shown in Fig. 4.1.

Meteorological input parameters

Evaluate the parameters in terms of required criterion Calculate 푅 푡푟푎푑. 푛푒푢푟푎푙 푛푒푡푤표푟푘푠 Identify the parameters suitable for node

Apply the suitable parameters to the suitable topology

Calculate 푅 푎푑푎푝푡푒푑 푛푒푢푟푎푙 푛푒푡푤표푟푘

Check the accuracy if No Yes 푅 푎푑푎푝푡푒푑 > 푅 푡푟푎푑. 푛푒푢푟푎푙 푛푒푢푟푎푙 푛푒푡푤표푟푘푠 푛푒푡푤표푟푘 Improve the Apply the network network’s accuracy to the sensor node

Fig.4. 1. Followed methodology for improvement of neural network’s accuracy

Neural networks using meteorological input parameters 62

4.3 Neural networks using meteorological input parameters

Many neural networks have been created by researchers to predict the solar energy (represented by 퐺푆푅 values) in different worldwide locations. These networks differ from each other in the structure (topology), the prediction time horizon, the location, the number and types of utilized meteorological input parameters. However, the most important networks will be shown in this section (Table 4.1) with a limited explanation about the meteorological input parameters utilized. This is attributed to the fact that it is the main focus of this work rather than the structure, site and the forecasting horizon. In the table, the abbreviations of the utilized input parameters are as next:

 Sunshine Duration (SD).  Air Temperature (AT).  Relative Humidity (RH).  Extraterrestrial Radiation (ER).  Atmospheric Pressure (AP).  Wind Speed (WS).  Diffuse Radiation (DR).  Cloud Cover (CC).

Table 4. 1. Different neural networks for calculating solar energy with their input parameters

Neural Network Input parameters SD AT RH ER AP WS DR 휽풛 CC Junliang et al. [121] × Belaid & Mellit [122] × Kee et al. [123] × Mellit I et al. [124] × × Khosravi et al. [125] × × Rehman & Mohandas [126] × × Ahmad I et al. [127] × × Ahmad II et al. [127] × × Marzo et al. [128] × × Behring et al. [129] × × × Tymvios et al. [130] × × × Mellit II et al. [131] × × × Shaddel et al. [132] × × × Gutierrez et al. [133] × × × × Assas et al. [134] × × × × × ×

Accuracy improvement of an adapted predictive neural network 63

4.4 Adaptation of predictive neural network using meteorological

input parameters for wireless sensor nodes

In order to adapt a neural network to be suitable for prediction in the wireless sensor nodes, two aspects should be identified, input parameters and topology.

4.4.1 Identifying of suitable meteorological input parameters

The suitability of different meteorological input parameters to be applicable with the wireless sensor nodes can be determined through the criterion of advance predictability as mentioned before. This criterion indicates basically to the ability to model the parameter of input. Thus, certain values for the modeled parameter will be defined in the microcontroller. The neural network will consider these values sequentially for the purpose of predicting 퐺푆푅 values.

Among all meteorological input parameters that are used in the aforementioned neural networks in Sec. 4.3, only zenith angle (휃푧) can be modeled in the microcontroller by a specific values. Thus, it allows the predictability in advance. Modeling is possible for this parameter because its values are extracted from the position of the sun which does not change for a certain location at the same time in a year. Since this parameter can be modeled in the microcontroller, it has an advantage of maintaining the system’s size constant without need to a measuring devices.

The other parameters like Extraterrestrial Radiation (ER), Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH), Atmospheric Pressure (AP), Cloud Cover (CC), Diffuse Radiation (DR), and Wind Speed (WS), do not have the feature of modeling. This is due to changing their values from one time slot to the next another randomly and without a fixed pattern. Fig. 4.2 shows the hourly variation of some of these parameters over a period of five years. Although that these parameters can also be predicted by the different prediction algorithms shown in Chapter 3, they are still not suitable to be used with wireless sensor nodes for the necessity of utilizing a measuring devices which increase the size of the sensor node.

Adaptation of predictive neural network using meteorological input 64 parameters for wireless sensor nodes

(a)

(b)

(c)

Fig.4. 2. Different patterns of meteorological parameters (a) Air temperature (b) Relative humidity (c) Diffuse radiation

Accuracy improvement of an adapted predictive neural network 65

4.4.2 Identifying the suitable topology

After identifying the suitable input parameter to be used with a predictive neural networks in the wireless sensor nodes, the number of neurons and layers should be known. Since increasing the number of hidden neurons and layers requires more space in the memory to be reserved as well as more computational efforts, the simplest topology (2 neurons and 1 hidden layer) will be used.

4.5 Data base for input and output parameters

After identifying the adapted neural network to be used with wireless sensor nodes, the accuracy of this network should be examined. However, the examining process requires a group of data. The data used for that are real data for the input and output parameters. These data are measured hourly for both parameters over five years (from 1st Jan, 2013 to 31st Dec, 2017) for the city of Chemnitz. This data are obtained by the German Weather Service (DWD) in [135]. These data are:

 퐺푆푅: measured in ( 퐽/푐푚2) and represented the output parameter (Fig 4.3).

 휃푧 : measured in (°) and represented the input parameter (Fig 4.4).

Fig.4. 3. Measured 퐺푆푅 over the period of (1st Jan, 2013 to 31st Dec, 2017)

Layout for modeling zenith angle as input parameter 66

st st Fig.4. 4. Measured 휃푧 over the period (1 Jan, 2013 to 31 Dec, 2017)

4.6 Layout for modeling zenith angle as input parameter

As the zenith angle (휃푧) needs a model for defining its values in the microcontroller which has a little memory, it is necessary to consider a layout for the intended model.

This allows to reduce the number of values to be defined for 휃푧. However, the suitable layout can be extracted by the deviations calculations. Consequently, the deviations between the parameter’s values for two sequent months as well as two sequent days in the same month have been analyzed.

th th th Fig. 4.5 shows the values of 휃푧 during two sequent days (15 and 16 April in (a), 15 and 16th October in (b)). It is clear that the curves are semi identical. Thus, the deviations are closed to zero during the whole 24 hours a day. This is also true for all months of the years. This means that progress of time from one day to another has ignorable effect.

In Fig. 4.6, the deviations between two different sequent months (April with May in (a), September and October in (b)) have been increased to significant values. Here, the effect can’t be ignored. This in role requires creating a daily model / curve for 휃푧 of monthly layout.

Accuracy improvement of an adapted predictive neural network 67

(a) (b)

Fig.4. 5. Change of 휃 between two sequent days in (a) April (b) October 푧

(a) (b)

Fig.4. 6. Change of 휃푧 between two sequent months (a) April and May (b) September and October

4.7 Modeling of zenith angle

The model of 휃푧 will be drawn as daily model for monthly layout. To this end, the monthly average of 휃푧 has been calculated at each daily hour. The twelve resulted daily curves have been fitted using a Gaussian function of two terms and six variables

(푎1, 푏1, 푐1, 푎2, 푏2, 푐2) which is appeared in (4.1). The values of these variables appear in Table 4.2 for the twelve months.

Modeling of zenith angle 68

푥−푏 푥−푏 −( 1)2 −( 2)2 푐 푐 휃푧 = 푎1 푒 1 + 푎2 푒 2 (4.1)

Table 4. 2. Values of modeling variables for 휃푧

Month 풂ퟏ 풃ퟏ 풄ퟏ 풂ퟐ 풃ퟐ 풄ퟐ Jan 149.7 0.665 10.36 157.2 27.47 11.92 Feb 141.5 1.002 10.12 143.2 26.33 10.60 Mar 131.0 1.343 9.746 129.0 25.39 9.408 Apr 119.1 1.687 9.213 115.2 24.63 8.376 May 110.0 1.940 8.741 105.5 24.17 7.700 Jun 105.6 2.065 8.493 101.1 23.97 7.398 Jul 107.4 2.017 8.596 103.1 24.06 7.530 Aug 114.7 1.811 9.001 111.1 24.43 8.078 Sep 125.7 1.503 9.527 123.6 25.07 8.975 Oct 137.2 1.152 9.977 138.1 25.96 10.14 Nov 147.1 0.786 10.28 153.2 27.11 11.52 Dec 151.7 0.567 10.41 161.8 27.88 12.38

The criteria considered for optimizing these values are the minimization of the Root Mean Square Error (푅푀푆퐸) which corresponds to the standard deviation of the difference between modeled and averaged values of 휃푧.

2 ∑푡=푇(푚표푑푒푙푒푑 휃 − 푎푣푒푟푎푔푒푑 휃 ) 푅푀푆퐸 = √ 푡=1 푧푡 푧푡 → 푚푖푛 (4.2) 푇

as well as the maximization of the coefficient of determination (푅2).

2 ∑푡=푇(푚표푑푒푙푒푑 휃 − 푎푣푒푟푎푔푒푑 휃 ) 푅2 = 1 − 푡=1 푧푡 푧푡 → 푚푎푥 푡=푇 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 2 (4.3) ∑푡=1(푚표푑푒푙푒푑 휃푧푡− 푎푣푒푟푎푔푒푑 휃푧푡)

Fig. 4.7 shows the changing in the values of (푅2) and (푅푀푆퐸) over the year’s months that correspond to the variables in Table 4.2. From this figure, (푅2) values are registered to be roughly 0.999 for the whole months. For (푅푀푆퐸), the highest value has been registered at June (1.9 퐽/푐푚2) and the lowest value is at February (0.44 퐽/푐푚2).

Accuracy improvement of an adapted predictive neural network 69

Fig.4. 7. Changing the values of 푅2 and 푅푀푆퐸 over the year with only two terms considered in 휃푧 model

Increasing the number of terms that contribute to describe or characterize the model of

휃푧 according to (4.4) will minimize the values of (푅푀푆퐸). This leads to more enhancement toward more accurate input parameter. Thus, more accurate predictive network. The change of these values over the year is clear in Fig. 4.8. For example, 푅푀푆퐸 does not exceeds 0.2 퐽/푐푚2 for five terms while they may touches 0.93 퐽/푐푚2 at three terms.

푥−푏 푥−푏 푥−푏 푥−푏 −( 1)2 −( 2)2 −( 3)2 −( 푛)2 푐 푐 푐 푐 휃푧 = 푎1 푒 1 + 푎2 푒 2 + 푎3 푒 3 + ⋯ + 푎푛 푒 푛 (4.4)

Fig.4. 8. Values of 푅푀푆퐸 over the year for different terms considered in 휃푧 model

Evaluation of prediction accuracy 70

4.8 Evaluation of prediction accuracy

In this section, the prediction accuracy of the adapted neural network will be evaluated. For the purpose of evaluating the accuracy, it will be compared with the accuracy of three main statistical algorithms as well as three traditional neural networks.

4.8.1 Accuracy of adapted neural network

In order to evaluate the prediction accuracy of the neural network that has 휃푧 as input parameter, the predicted 퐺푆푅 values by this neural network need to be calculated firstly. Fig. 4.9 shows these predicted values for the same city “Chemnitz” and the same time period (1st Jan, 2013 to 31st Dec, 2017).

Fig.4. 9. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by adapted neural network that has 휃푧 as input parameter

The accuracy of the neural network that has 휃푧 as input parameter can be found by correlating the predicted 퐺푆푅 values in Fig. 4.9 with the measured values in Fig. 4.3. The correlating is expressed by a correlation coefficient called (푅). As the value of 푅 increasing toward 1, the correlation becomes more linearly and the results of prediction become more accurate. However, the correlation between these two figures is shown in Fig. 4.10. From this figure, the network has registered a value of 0.865 for 푅.

Accuracy improvement of an adapted predictive neural network 71

Fig.4. 10. Correlation between the predicted and measured 퐺푆푅 for adapted neural st st network of 휃푧 input parameter over the period (1 Jan, 2013 to 31 Dec, 2017)

Consequent to this calculations, the corresponding prediction error expressed as a percent is calculated and figured in Fig4. 11. From the figure, it is clear that the maximum registered error during this period is 9%.

Fig.4. 11. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by adapted neural network that has 휃푧 as input parameter

Evaluation of prediction accuracy 72

4.8.2 Accuracy of main statistical algorithms

In this subsection, three main statistical algorithms will be discussed and analyzed. These algorithms have been chosen to be EWMA, WCMA and Pro-Energy algorithms because they are the basic ones in the three aforementioned statistical groups. However, it is necessary in the beginning to find the optimal value of the weighting parameter (훼) for the city of Chemnitz. This is to ensure using a weighting parameter that contribute to fulfill the minimum prediction error. For this purpose, an average of the prediction errors is calculated for different values of (훼) as in Fig. 4.12. From this figure, it is clear that the suitable value of (훼) is 0.7.

Fig.4. 12. Average of prediction errors for different values of 훼

After that, the results of predicting 퐺푆푅 values for the period (1st Jan, 2013 to 31st Dec, 2017) are calculated using these statistical algorithms considering (훼 = 0.7) for all of them. These results are figured in Fig. 4.13. In order to evaluate the prediction accuracy for the selected statistical algorithms, the measured values in Fig. 4.3 for 퐺푆푅 should be correlated with the predicted results in Fig. 4.13. The resulted correlation coefficient (푅) for these algorithms has been registered to be 0.603 for EWMA, 0.730 for WCMA and 0.819 for Pro-Energy (Fig. 4.14). Accordingly, Pro-Energy algorithm shows the highest value compared to the others EWMA and WCMA. By comparing these values of correlation coefficient with the correlation coefficient of the adapted neural network in Fig. 4.10, it is found that all of them is less than it. Thus, the neural network that has an input parameter of 휃푧 is more accurate.

Accuracy improvement of an adapted predictive neural network 73

(a)

(b)

(c)

Fig.4. 13. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) EWMA (b) WCMA (c) Pro-Energy)

Evaluation of prediction accuracy 74

(a) (b)

(c)

Fig.4. 14. Correlation between the predicted and measured 퐺푆푅 for ((a) EWMA (b) WCMA (c) Pro-Energy) over the period (1st Jan, 2013 to 31st Dec, 2017)

Similarly to the previous analysis, the prediction errors are calculated for the main statistical algorithms and figured in Fig. 4.15. From the figure, the maximum prediction errors have been registered respectively for EWMA, WCMA and Pro-Energy to be 33%, 24% and 15%. These numbers also show the prediction accuracy levels for these algorithms. From other side, they confirm the facts that EWMA has low accuracy, Pro- Energy has higher accuracy and WCMA has an accuracy in between.

Accuracy improvement of an adapted predictive neural network 75

(a)

(b)

(c)

Fig.4. 15. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) EWMA (b) WCMA (c) Pro-Energy)

Evaluation of prediction accuracy 76

4.8.3 Accuracy of traditional neural networks

The traditional neural networks mainly utilize the last harvested energy observations as input parameters to predict the harvestable energy in the upcoming time slot. Thus, more observations considered for the prediction process refer to more accurate networks. However, three traditional neural networks (denoted by NN1, NN2 and NN3) will be defined and evaluated in terms of accuracy within in this sub-section.

The size of the last harvested energy observations that are considered as input parameters differ for the traditional networks NN1, NN2 and NN3. Fig. 4.16 explains the harvested energy observations considered in each one of these networks as input parameters (the green colored slots). From this figure, NN1 considers only 2 input parameters from the past days, 5 input parameters for NN2 (one in the current day and 4 in the past days) and 19 input parameters for NN3.

(b) (a)

(c) Fig.4. 16. Harvested energy observations considered for the traditional neural networks (a) NN1 (b) NN2 (c) NN3

For the same city (Chemnitz), these observations have been collected from Fig 4.3 over the same period (from 1st Jan, 2013 to 31st Dec, 2017) and applied to three networks of the simplest topology. The resulted predicted 퐺푆푅 values are calculated using these networks and figured in Fig. 4.17.

Accuracy improvement of an adapted predictive neural network 77

(a)

(b)

(c)

Fig.4. 17. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) NN1 (b) NN2 (c) NN3)

Evaluation of prediction accuracy 78

In order to examine the accuracy of the previous methods, the measured 퐺푆푅 values in Fig. 4.4 and the predicted 퐺푆푅 values from Fig. 4.17 have been correlated to each other. For evaluating the accuracy, the results of correlating Fig 4.3 with Fig. 4.17 (the measured and predicted values of 퐺푆푅) have been calculated and figured in Fig. 4.18. From this figure, the correlation coefficient (푅) has been registered 0.963 for the traditional neural network NN1, 0.845 for NN2 and 0.945 for NN3. Thus, it is clear that NN3 is the most accurate one among the considered traditional neural networks, the adapted neural network and the main statistical ones. The prediction errors of these traditional networks are also calculated over the same period and the same city “Chemnitz” in order to realize them. These errors are figured in Fig. 4.19.

(a) (b)

(c)

Fig.4. 18. Correlation between the predicted and measured 퐺푆푅 for ((a) NN1 (b) NN2 (c) NN3) over the period (1st Jan, 2013 to 31st Dec, 2017)

Accuracy improvement of an adapted predictive neural network 79

(a)

(b)

(c)

Fig.4. 19. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) NN1 (b) NN2 (c) NN3)

Accuracy improvement of adapted neural network 80

Fig. 4.19 shows that the maximum prediction error has registered respectively for NN1, NN2 and NN3 to be 12.3%, 6.5% and 5%. Thus, these percentages confirm again that the traditional network NN3 is the most accurate algorithm compared to the other traditional networks and the main statistical ones.

In light of this evaluation that handles the correlation coefficients and the prediction accuracy for different statistical algorithms as well as different traditional neural networks, it can say that the percentage of 5% maximum error is still considerable and leads to a disparity in the operating states of the sensor nodes. Thus, an improvement is needed to be done. The target of this improvement is to fulfill a prediction errors lower than this percentage. This in role will reflects an improving in the operating states to make the sensor node more effective and reliable one. The starting point for this improvement will based on using the adapted network that has input parameter휃푧. The proposed improvement is explained in the next section Sec. 4.9 with more details.

4.9 Accuracy improvement of adapted neural network

Since the accuracy of the adapted neural network does not exceeds the accuracy of the traditional neural networks. The target now is to search about a way to improve the accuracy of the adapted neural network without adding measuring devices. This requires looking for input parameters which are strongly related to the solar radiation over the day and their values can be defined in the microcontroller.

From equation (2.1) that describes the solar radiation behavior, the parameter 휃푧 is the only parameter that changes over the day with changing the sun’s position. The diffuse radiation needs a pyrometer to measure its values. From other side, all other meteorological parameters require measuring devices and could not be modeled.

It is worth noting that increasing the number of layers as well as hidden neurons has a significant role to reduce the number of iterations required for calculating the final weights and biases. Thus, the accuracy represented by the value of 푅 does not highly improved. Because the training process will be implemented in a larger machine, the number of iterations is incidental. Accordingly, the solution to improve the accuracy of the neural network of 휃푧 is to add another input parameter from the results of statistical prediction algorithms. The next sections will explain the mechanism of selecting the suitable statistical algorithms as well as evaluating the accuracy of the neural network improved.

Accuracy improvement of an adapted predictive neural network 81

4.9.1 Selection of the suitable improving statistical algorithm

In this section, a criteria of selection the statistical algorithm for improving the adapted neural network is explained.

As the prediction error and the execution time are the most important performance indices for different prediction algorithms, both of them will be used for the process of selection. As mentioned before, the algorithm of the lowest prediction error and the lowest execution time refers to the highest accuracy and lowest power consumption. Thus, it is the desired and most suitable one.

In order to evaluate these two indices together at the same time for different prediction algorithms, the values of the maximum prediction error and execution time from Table 3.2 will be plotted as in Fig. 4.20. The intersection point of the two lines that describe these two indices is an indication the suitable algorithm. From this figure, Pro-Energy is the most suitable one to be added as second input parameter to the neural network.

80.00 0.800 Prediction error Execution time 70.00 0.700

60.00 0.600

50.00 0.500

40.00 0.400

30.00 0.300

20.00 0.200 time secin Execution Maximum prediction % in error prediction Maximum 10.00 0.100

0.00 0.000

Fig.4. 20. Maximum prediction error and execution time versus prediction algorithms

Accuracy improvement of adapted neural network 82

4.9.2 Evaluation the accuracy of improved neural network

Since Pro-Energy is the most suitable algorithm among the statistical algorithms, it will be chosen to add as a second input parameter. The results of predicting 퐺푆푅 values by the proposed neural network (i.e. the network of 휃푧 + Pro-Energy) is calculated for the same period (from 1st Jan, 2013 to 31st Dec, 2017) and figured in Fig. 4.21. The accuracy of the proposed network can be calculated by correlating the predicted values in Fig. 4.21 with measured values in Fig. 4.3. Thus, the correlation coefficient 푅 = 0.992 as in Fig. 4.22. This value exceeds the value 0.963 for the most accurate traditional neural network NN3.

Fig.4. 21. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by improved neural network of input parameters (휃푧 & Pro Energy results)

Realizing the prediction error resulted from the neural network improved is also necessary for validating process. To this end, the perdition errors caused are calculated and figured in Fig. 4.23. This figure shows a maximum prediction error of 2%. This percentage is less than the all aforementioned percentages in the statistical algorithms, the traditional and the adapted neural networks.

Accuracy improvement of an adapted predictive neural network 83

Fig.4. 22. Correlation between the predicted and measured 퐺푆푅 by improved neural st network of input parameters (휃푧 & Pro Energy results) over the period (1 Jan, 2013 to 31st Dec, 2017)

Fig.4. 23. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by improved neural network of input parameters (휃푧 & Pro Energy results)

Accuracy improvement of adapted neural network 84

4.9.3 Weights and biases of improved neural network

For the training process, the activation function has been chosen to be “Logsig” function. This is due to the fact that the output parameter represented by global solar radiation (퐺푆푅) do not has negative values even in the night. The real values are either positive or zero. After training the network of (휃푧 & Pro-Energy results) in a computer, six weights (for edges) and three biases (for neurons) will arise. It is noteworthy here that the total number of weights and biases are less than it for the aforementioned traditional networks.

Furthermore, considering arrangement of neurons and input parameters is important to specify the values of them. For example, when consider the resulted predicted values by Pro-Energy algorithm for the first input, the modeled 휃푧 for the second input and the numbering of neurons as in Fig. 4.24, the final resulted weights and biases are respectively shown in Table 4.3 and Table 4.4. In these tables, only 푤11 , 푤13 and 푏1 have appositive values. The highest weight value is recorded to be 26.2080 for 푤13. Thus, it has the stronger impact in the prediction process. Far away from considerations of sign and impact strength, these values will copy to the code in order to make the neural network executable in the microcontroller.

푏1

푏 Output 푤11 3 Pro-Energy 1 Input 1

3

휃 푧 푤22 Input 2 2

푏 2 Neuron

Fig.4. 24. Arrangment of neurons and input parameters considered for the simple topology

Accuracy improvement of an adapted predictive neural network 85

Table 4. 3. Weights of improved neural network

Weight 풘ퟏퟏ 풘ퟏퟐ 풘ퟐퟏ 풘ퟐퟐ 풘ퟏퟑ 풘ퟐퟑ Value 5.3341 -0.5769 -0.0581 -0.0023 26.2080 -23.1734

Table 4. 4. Biases of improved neural network.

Bias 풃ퟏ 풃ퟐ 풃ퟑ Value 7.0147 -0.4317 -35.5993

Implementing of the proposed predictive neural network in sensor nodes 87

5 Implementing of the proposed predictive neural

network in sensor nodes

In this Chapter, the influence of applying aforementioned traditional and proposed neural networks on managing the consumption of energy in solar powered wireless sensors has been studied. To this end, the operating states of a neutrality energy management scheme have been defined firstly in Sec. 5.1. In Sec. 5.2, the conditions adopted for operation have been displayed. Sec. 5.3 shows a description about the hardware utilized for the purpose of implementing the predictive networks. Sec. 5.4 compares the prediction errors caused by the proposed predictive neural network and the traditional ones over a selected simulation period. Lastly, the resulted operating states have been monitored and analyzed during the same period in Sec. 5.5.

5.1 Definition of operating states

Usually, the energy supplied by solar cells are not significantly influenced in the small scale autonomous energy systems. For this reason, there is a greater desire to regulate energy consumed than energy supplied. Thus, the energy management scheme must be able to deal with the energy available to it. However, the scheme that can be adopted for the task of management is the “neutrality scheme” which is described in [136]. According to this scheme, the wireless sensor node can be adjusted into one of eight operating states or modes. Each one of these states has a specific consumption level or range of energy to work on. Fig. 5.1 shows the sequence of these states according to their consumption levels. A detailed description for each state is shown in the following:

 Switch off mode (S0): The energy available for this state ranges (0 ≤ 퐸푎푣푎 < 퐸푖푛푖 ), i.e. the available energy is insufficient or does not reach initial energy, the required energy to operate the sensor node. Thus, the sensor node will be turned off at this state.

Definition of operating states 88

 Permanent sleep mode (S1): The energy available for this state increase to a

value within the range (퐸푖푛푖 ≤ 퐸푎푣푎 < 퐸푚푖푛 ). This state refers to the basic consumption where the cyclic operation for the measurement / transmission processes is not possible.

 Minimum operation mode (S2): The energy increase in this state to touch the

minimum consumption level (퐸푎푣푎 = 퐸푚푖푛). The node becomes able to implement the required task. At this state, a small amount of energy is collected until it becomes sufficient to perform a measurement / transmission process. As the processes are irregularly occurred at this state, the cyclic operation is described to be uncontrolled.

 Reduced functionality mode (S3): This state referred to an energy consumption

ranges between the minimum and nominal levels (퐸푚푖푛 < 퐸푎푣푎 < 퐸푛). The node works here but under reduced functionality condition and controllable cyclic operation of the measurement / transmission processes.

 Normal operation mode (S4): The energy available equals the nominal

level (퐸푎푣푎 = 퐸푛), (i.e. the energy needed by the sensor node is equivalent the energy available). The node here is described with balance situation. The sensor node works at this state with full functionality and controllable cyclic operation. So, this state is the desired normal operating state.

 Extended functionality mode (S5): This state indicates to an extended functionally mode. The energy level for this state ranges between the nominal

and maximum consumption (퐸푛 < 퐸푎푣푎 < 퐸푚푎푥). Additionally, the cyclic operation is possible and controllable.

 Maximum operation mode (S6): The energy reached (퐸푎푣푎 = 퐸푚푎푥 ) level in this state. The sensor node works at maximum ability with uncontrolled duty cycle. This state is considered the second highest consumption level.

 Continuous operation mode (S7): This is the last operating mode and considered a theoretical operation mode. Within this mode, the energy consumption corresponds to the total energy available from the source. This mode is necessary to protect the sensor system against any energy exceeds the needed energy. At this state, a permanent transmission of the data is occurred.

Implementing of the proposed predictive neural network in sensor nodes 89

Permanent Transmission S7 Continuous Operation Mode

Operation

) 퐸푚푎푥 Uncontrolled 푎푣푎 S6 Maximum Operation Mode

퐸 Cyclic Operation ( S5 Extended Functionality Mode 퐸 Controlled 푛 S4 Normal Operation Mode

Energy Cyclic Operation

S3 Reduced Functionality Mode Uncontrolled 퐸푚푖푛 S2 Minimum Operation Mode Available Cyclic Operation S1 Permanent Sleep Mode No Cyclic Operation 퐸푖푛푖

0 S0 Switch Off Mode

Fig.5. 1. Operating modes or states of neutrality energy management scheme in wireless sensors node

5.2 Operating conditions

A real wireless sensor node has been chosen to implement the neutrality energy management scheme that mentioned in Sec. 5.1, with the traditional and proposed predictive neural networks. For this node, values of the operating parameters are needed to be known in order to identify the limits of the operating modes. However, the operating parameters considered for implementation are:

 Operating Voltage = 3.3 volt

 퐼푖푛푖 = 10 µ퐴/푠  퐼푚푖푛 = 15 푚퐴/푠  퐼푛 = 77.3 푚퐴/푠  퐼푚푎푥 = 120 푚퐴/푠

According to the previous operating parameters, the wireless sensor has been adjusted to the following energy levels over one complete hour:

 퐸푖푛푖 = 0.1188 퐽/ℎ표푢푟  퐸푚푖푛 = 178.2 퐽/ℎ표푢푟

Operating conditions 90

 퐸푛 = 918.3 퐽/ℎ표푢푟  퐸푚푎푥 = 1425.6 퐽/ℎ표푢푟

The wireless sensor node, chosen for implementation, is supplied by a solar cell of 5 푐푚2 area. This source is supported with a rechargeable battery. Thus, the solar cell and the battery will work together to operate the sensor node. The energy stored in the battery (퐸퐵) is expressed by energy available in the battery at initial state (퐸퐵0) and the difference of supplied and consumed energies (퐸푠푢푝, 퐸푐표푛), respectively as in (5.1).

퐸퐵 = 퐸퐵0 + (퐸푠푢푝 − 퐸푐표푛) (5.1)

Equation (5.2) reformulates (5.1) to consider an individual and subsequent time slots for the energy stored in the battery.

퐸퐵(푛 + 1) = 퐸퐵(푛 − 1) + (퐸푠푢푝(푛) − 퐸푐표푛(푛)) (5.2)

In order to protect the battery against any energy from the solar cell exceeded its maximum capacity (퐸퐵푚푎푥), equation (5.3) has been applied.

퐸퐵푚푎푥 , 퐸퐵(푛 + 1) ≥ 퐸퐵푚푎푥 퐸퐵(푛 + 1) = { (5.3) 퐸퐵(푛 + 1) , 퐸퐵(푛 + 1) < 퐸퐵푚푎푥

Since the solar cell feeds the battery and the node is fed by the battery, the energy available to the node is expressed by:

퐸푎푣푎(푛 + 1) = 퐸퐵(푛 + 1) (5.4)

For the modes of cyclic operation, linking energy consumption with energy available is necessary. This can be performed through a controlling variable, referred to as duty cycle as in (5.5). Usually, the duty cycle is adjusted to a certain desired value at the normal operation mode. This value increases toward the maximum operation mode and decreases toward the minimum operation mode. However, at the cases of permanent transmission and permanent sleep, the duty cycle corresponds to the values 1 and 0, respectively. This means that when the node is slept, it collects energy for itself but does not consume it. Additionally, when it is switched off, the duty cycle is not considered.

퐸푐표푛(푛) = 푑푢푡푦 푐푦푐푙푒 × 퐸푎푣푎(푛) (5.5)

As the transmission of data consumes more energy than measuring, the duty cycle is adjusted to a shorter time for transmission. For the considered case, the duty cycle at

Implementing of the proposed predictive neural network in sensor nodes 91 the normal operation corresponds to: 1 second measurement / 0.5 second transmission i.e. 33%. In addition, the normal operation mode has been considered to have a range of ± 10% around 퐸푛.

5.3 Implementation on hardware

The utilized module as wireless sensor unit for the purpose of implementation and simulation is (ESP8266-12F). This module includes a microcontroller of 32 bit which is programmable by Arduino. The prediction algorithms as well as the neutrality energy management scheme are carried out by written codes. This module includes also a wireless communication unit which represents a transceiver for the data. This module is cheap and widely used with the solar powered sensor nodes. It also allows the reduction on energy consumption through a sleep operation mode. Energy harvesting unit is also needed for the real implementation of the sensor node as a hardware. To this end, ultra-low power harvester called “bq25570” is used. This is an Integrated Circuit (IC) manufactured by the company “Texas Instruments”. It includes power management unit, MPPT unit as well as buck and boost converters. This IC is compatible to use with solar cells and can be supplied with a batteries as a storage unit. The electrical circuit for connecting this unit with other components (solar cells, battery, wireless sensor unit, resistors…etc.) is clear in Fig. 5.2. Additionally, the real implemented hardware for this circuit is shown in Fig. 5.3.

Fig.5. 2. Electrical circuit of solar powered sensor node

Implementation on hardware 92

Fig.5. 3. Real hardware of solar powered sensor node

The main challenge during implementing the codes of the different predictive algorithms in the microcontroller is the limitations of the hardware, especially with the neural networks based ones. This challenge has been overcome as mentioned before through an off board training process. Another challenge relates to availability of a sufficient memory space. This memory should has enough space for modeling 휃푧 as well as for recording the observations about the previously harvested energy. In addition, there is a need for special mathematical libraries to be defined. The exponential functions library, neural networks library as well as the library for the ESP8266-12F module are examples of these libraries. All of these libraries are not part of the standard instructions set of the Arduino microcontroller and need to be included at the beginning of the written code.

Using the implemented hardware, three main statistical algorithms, three traditional neural networks and the proposed neural network have been carried out. For all of them, the processing / execution time and the reserved spaces in the memory are evaluated and shown in Table 5.1. From the table, EWMA algorithm shows the lowest execution time and the lowest reserved space at all with 0.01 second and 96 bytes, respectively. For WCMA algorithm, 0.051 second and 384 bytes are registered. Pro-

Implementing of the proposed predictive neural network in sensor nodes 93

Energy algorithm needs more time and space compared with EWMA and WCMA algorithms. From other side, the execution time of Pro-Energy algorithm is less than it for all traditional and proposed neural networks, while the memory space for NN1 and NN2 is less than it in the Pro-Energy algorithm. NN3 needs memory space 32 bytes more than Pro-Energy. The proposed neural network shows the highest reserved space (884 bytes), while its execution time is greater than NN2 and less than NN3. The next section discuss the results of the prediction accuracy through calculating the prediction errors.

Table 5. 1. Reserved space and processing time for different prediction algorithms utilized in wireless sensor nodes

Prediction Execution Reserved space Algorithm time in the memory in (sec) in (byte) EWMA 0.010 96 WCMA 0.051 384 Pro-Energy 0.160 488 NN1 0.323 396 NN2 0.485 480 NN3 0.711 520 Proposed NN 0.636 884

5.4 Prediction errors

This section shows the results of predicting the harvested energy represented by 퐺푆푅 values using the aforementioned three main statistical algorithms, the three traditional neural networks and the proposed neural network at the same conditions of time and place. Fig. 5.4 shows these results for the city of Chemnitz over a simulation period of one week (from 15th July 2018 to 21st July 2018) compared to the actual harvested energy. It is worth note that 훼 is chosen to be 0.7 for the main statistical algorithms EWMA, WCMA and Pro-Energy. The corresponding deviations between the actual and the predicted values are shown in Fig.5.5. These deviations might be positive or negative according to the predicted values which are greater or lower the actual ones. From the figure, the highest deviation is registered for EWMA with 78.44 J/푐푚2 at the last day. The most accurate neural network NN3, has registered a deviation of 23.51 J/푐푚2 at the fourth day. The proposed neural network has the lowest deviation ( 7.54 J/푐푚2) which appears at the third day.

Prediction errors 94

Fig.5. 4. Predicted energy by different predictive neural networks over the summer week (15th July 2018 to 21st July 2018)

Fig.5. 5. Deviation between the actual and predicted values of different prediction algorithm over the summer week (15th July 2018 to 21st July 2018)

Implementing of the proposed predictive neural network in sensor nodes 95

In order to realize the prediction errors caused by the aforementioned prediction algorithms, equation (5.6) is used for calculating their values over the week of simulation.

퐴푐푡푢푎푙 퐸푛푒푟푔푦−푃푟푒푑푖푐푡푒푑 퐸푛푒푟푔푦 푃푟푒푑푖푐푡푖표푛 퐸푟푟표푟 = | | × 100% (5.6) 퐴푐푡푢푎푙 퐸푛푒푟푔푦

The calculated prediction errors have been figured in Fig. 5.6. For this figure, the maximum caused prediction error, expressed as a percentage, will be considered as a criterion for evaluation. From this figure, EWMA algorithm has registered a maximum error 32.5% at the last day. Compared to EWMA, the algorithm of WCMA was able to reduce the prediction error to 23.1%. In the third degree, Pro-Energy is coming with 10% at the last day too. On the side of traditional neural networks, the maximum prediction error during the simulation period of NN1, NN2 and NN3 are recorded 9%, 5%, 3%, respectively. The proposed neural network has registered a maximum prediction error at the fourth day with a percentage 1%. Thus, it shows the lowest prediction error at all.

Fig.5. 6. Prediction error of different predictive neural networks over the summer week (15th July 2018 to 21st July 2018)

Although that the previous percentages show that NN2 is more accurate than the main statistical algorithms, it can show sometimes an error greater than it in the main

Distribution of operating states 96 statistical algorithms. This is appeared clearly at the sunset periods of the fourth and sixth days. Also, the traditional neural network NN3 shows an error higher than it for NN2 at the mid of the last day. However, these exceptions do not happened with the proposed neural network. Thus, it still has preference over the all aforementioned statistical algorithms and traditional neural networks.

5.5 Distribution of operating states

In this section, the neutrality energy management scheme stated in Sec. 5.1 has been applied considering the operating conditions in Sec. 5.2 with and without prediction. The results of distributing the operating states have been monitored over the same simulation week (from 15th July 2018 to 21st July 2018). Starting with the case of no prediction (applying the energy management scheme only), Fig. 5.7 shows that the sensor node worked 84 hours in the normal operation state (S4). The operation states (S3) and (S5) have been registered 38 hours and 31 hours, respectively. S2 is coming after that with 10 hours and lastly 5 hours for S6. From the figure, the operating states S0, S1 and S7 do not recorded any time over the period of simulation. This is positive index because the sensor does not switched off, slept or worked under permanent transmission.

Fig.5. 7. Distribution of operating states without prediction

In the other case, where the prediction is considered beside the management scheme, the results of distributing the operating states have been recorded for the three main statistical algorithms, the three traditional neural networks and the proposed neural network mentioned before. Fig. 5.8 and Fig. 5.9 show respectively these results for the three main statistical algorithms as well as the three traditional neural networks over the same simulation week (from 15th July 2018 to 21st July 2018). Additionally, the

Implementing of the proposed predictive neural network in sensor nodes 97 prediction has been considered to be implemented every hour for all of these prediction algorithm.

For the statistical algorithm EWMA, Fig. 5.8 (a) indicates that the sensor node has worked 119 hours within the normal operating state (S4). This value is increased to 127 hours for WCMA (Fig. 5.8 (b)) and 138 hours for Pro-Energy algorithm (Fig. 5.8 (c)). For all of these algorithms, these values were accompanying with decreasing in the values of the other registered operation states. For example, S3 is decreased from 20 hours in case of EWMA to 18 in WCMA and 15 in Pro-Energy. Also, S5 is decreased from 12 hours in case of EWMA to 10 in WCMA and 8 in Pro-Energy. All the statistical algorithm in this figure have an advantage of no registered hours for the operating states S0, S1 and S7.

(a) (b)

(c)

Fig.5. 8. Distribution of operating states by ((a) EWMA (b) WCMA (c) Pro-Energy) over the summer week (15th July 2018 to 21st July 2018)

Distribution of operating states 98

Regarding the results of the distribution of the operating states under neural networks predictive conditions, Fig. 5.9 (a) shows that NN1 force the sensor node towards more operation in normal state (S4) compared to all statistical algorithms in the Fig. 5.8 (148 hours). The traditional neural networks NN2 and NN3 have recorded 155 hours and 160 hours, respectively for this state (Fig. 5.9 (b) and Fig. 5.9 (c)). Thus, NN3 is the best traditional network among all statistical and neural networks considered. This actually back to its high prediction accuracy.

The proposed neural network was able to operate the implemented sensor node 6 hours more in the state of normal operation during one week of simulation (166 hours). Only one hour is registered for each of S3 and S5 for this network and no operation within S2 and S6. One more time, the sensor node do not appear any operation for the states S0, S1 and S7 for all networks in Fig. 5.9 .

(a) (b)

(c) (d)

Fig.5. 9. Distribution of operating states by ((a) NN1 (b) NN2 (c) NN3 (d) Proposed NN) over the summer week (15th July 2018 to 21st July 2018)

Implementing of the proposed predictive neural network in sensor nodes 99

Since the operation of the sensor node with full functionality is occurred within the operation state S4, it is the desired state to operate the node all the time. According to the values registered in (Fig. 5.7 to Fig. 5.9) for this state, it is clear that the prediction shows a performance better than the case of no prediction. Additionally, the proposed neural network is considered better than the statistical prediction algorithms and traditional neural networks. Compared to the most accurate traditional neural network NN3, the proposed predictive neural network can enhance the performance with roughly 3.6%.

Conclusions and future works 101

6 Conclusions and future works

This chapter summaries the work that is implemented in the thesis through two sections: Sec. 6.1 shows the conclusions and Sec. 6.2 shows the future works.

6.1 Conclusions

The last decade has witnessed several significant developments in the field of wireless sensor networks. These developments were not only limited to minimize the sensor’s size and to enhance the communication paths between nodes, but also fulfilling an energy-efficient sensor nodes. In this regards, using batteries as fixed and alone power source to feed the nodes with energy is no longer feasible. This is due to the limited amount of energy available as well as the frequent and expensive visits for the purposes of replacement and maintenance. To overcome this obstacle, harvesting an ambient energy has been adopted as a solution to support the batteries. Solar energy harvesting systems are the most promising and attractive ones as the solar energy has the highest power density among different energy forms and applicable in outdoor environments.

Utilizing of solar energy harvesting systems with wireless sensor nodes has also a significant challenge. This is mainly due to the ambiguous about the amount of the harvestable solar energy which is needed to be known in advance for a reliable operation of the nodes. In other words, the fluctuating nature of solar energy and its interruptions which are caused by the clouds, shadow effects and atmospheric factors have a negative and direct effect on the reliability of that nodes. Consequently, an energy management schemes (operation schemes) that based on a prior prediction for solar energy have been created to enhance the reliability. Although that the prediction algorithms are different and variant, they are still provide inaccurate prediction which does not fit the purpose in many applications of solar powered wireless sensors.

The wireless sensor nodes as a small power systems are characterized with a specific features. For example, they are designed to be small sized systems. The more size

Conclusions 102 systems the more expensive ones and the more limited applications. Additionally, the small memory that embedded with the node’s microcontroller. These features in addition to others restrict the implementation of prediction algorithms in that nodes.

From the state of the art, many neural networks are available to calculate the global solar energy at a certain time. They differ from each other in the topology, input parameters and the location. All of them are traditional and use the last observations of harvested energy as input parameters. This thesis proposes a new predictive neural network for managing the energy consumption of solar powered wireless sensor node. The proposed network is designed to fulfill accurate prediction compared to the traditional networks and the statistical algorithms. This requires choosing meteorological input parameters for the network that fulfil the most important criterion “predictability”. Mainly, these input parameters should have the ability to be modeled which is only possible for solar zenith angle. Additionally, the simplest topology has been selected to implement on microcontroller in order to ensure that it fits with the small memory available.

The neural network of solar zenith angle, as only input parameter, fulfills the predictability criterion. Although that the accuracy level of this network is higher than the statistical algorithms, it is less than some traditional neural networks. Accordingly and because there is no other meteorological parameter has a certain pattern to model, this neural network is improved by adding the resulted predicted values by Pro- Energy algorithm as a second input parameter. The improved neural network has registered an accuracy level exceeds the whole traditional networks and the statistical algorithms. In addition, it also can keep the size of the system constant without a need to measuring devices.

Finally, a neutrality scheme for managing the consumed energy in solar powered wireless sensor node has been implemented using the improved predictive neural network, the traditional neural networks and the main statistical algorithms. This is performed to study the effect of the improved neural network on the management schemes compared to different prediction algorithms. This work shows that the improved network is playing a significant role to enhance the operation of the sensor nodes in a correct operation modes. This supports the sensor nodes toward more functional and reliable operation.

Conclusions and future works 103

6.2 Future works

In the future, the power supply of wireless sensor systems will continue to improve with new, hybrid and more efficient technologies and advances in energy converters. This also opens up further optimization possibilities for new input parameters to be considered for the neural based prediction. This may help the researches to avoid using the traditional networks as well as the stochastic and statistical algorithms. Thus, reducing the processing or execution time.

Furthermore, the developments toward higher performance microcontrollers and generally more efficient one will further reduce the power consumption and allow on board training for the neural networks. Additionally, a small measuring devices may be embedded with the microcontroller board which in its role will allow more predictive neural networks to be implemented with wireless sensors keeping the system’s size constant.

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Thesen

 Energy harvesting systems can reduce the cost of deploying wireless sensor networks. This is fulfilled through replenish the composed sensor nodes with energy in the field. This in role will reduce the cost of maintenance and replacement of batteries.

 Solar cells are the most promising and attractive harvesting systems to be used with sensor nodes. This due to the high power density, the low cost of solar panels and the possibility to use in outdoor environment.

 Energy consumption management in solar powered wireless sensor networks is necessary to fulfill functional, effective and reliable sensor nodes.

 The most common management scheme is based on a prior knowledge of harvesting opportunities. This mainly indicates to the principle of predicting solar energy represented by global solar radiation.

 The prediction of solar energy in solar supplied sensor nodes can be implanted using stochastic, statistical and artificial intelligence algorithms.

 The stochastic algorithms cause large prediction errors. These errors are due to the discreet states which are mostly do not fit the actual readings.

 The statistical algorithms have also considerable errors. These errors appear when a sudden change in the weather is occurred. Thus, they are convenient to predict energy profiles under consistent weather conditions. Additionally, the weather conditioned moving averages show a high prediction error at periods of sunrise and sunset.

 Artificial intelligence algorithms represented by neural networks have the lowest prediction errors compared to the stochastic and statistical ones. Although that, they are implemented in traditional way through considering the previous observations as input parameters. This in role requires large space in the memory of the microcontroller and provide limited accuracy level.

 This thesis develops a neural network of improved accuracy by considering input parameters of meteorological parameter represented by “zenith angle” and statistical algorithm represented by “Pro-Energy”.

Thesen 119

 Selecting “zenith angle” among the meteorological parameter is back to the ability to predict this parameter. Thus, their values can be modeled and supplied to the microcontroller.

 Selecting “Pro-Energy” among the statistical algorithms to be combined with the zenith angle is due to the low execution time and low prediction accuracy.

 Using the neural network developed in this thesis contributes to improve the management of solar powered wireless sensor nodes. This is appear through more operating for the sensor node in the desired normal operation.

List of Figures 121

List of Figures

Fig.1. 1. Structure of thesis ...... 5

Fig.2. 1. Structure of wireless sensor network ...... 8 Fig.2. 2. Structure of sensor node ...... 9 Fig.2. 3. Schematic diagram of energy management schemes in WSN ...... 12 Fig.2. 4. Levels of energy management in WSN ...... 13 Fig.2. 5. Components of energy management in wireless sensor nodes ...... 14 Fig.2. 6. Types of solar radiation ...... 17 Fig.2. 7. Solar radiation patterns in (a) cloudy day (b) clear sky day ...... 18 Fig.2. 8. Changing of declination angle over a year...... 20 Fig.2. 9. Angular relationships of the solar day ...... 21 Fig.2. 10. Path length of different air mass coefficients ...... 25 Fig.2. 11. Reflections of direct radiation on a solar surface (absorber) ...... 26 Fig.2. 12. Solar angles for tilted solar cell ...... 27 Fig.2. 13. Real small scale solar cell ...... 29 Fig.2. 14. Equivalent circuit of solar cell ...... 29 Fig.2. 15. Voltage / current characteristics of a solar cell ...... 31

Fig.3. 1. Schematic diagram for prediction algorithms classification...... 34 Fig.3. 2. Historical progress of solar energy prediction algorithms in wireless sensor nodes ...... 35 Fig.3. 3. Transition probabilities of the Markov chain...... 36 Fig.3. 4. Graphical representation of EWMA algorithm ...... 38 Fig.3. 5. Graphical representation of SEP-DR algorithm ...... 39 Fig.3. 6. Graphical representation of WCMA algorithm ...... 42 Fig.3. 7. Graphical representation of Pro-Energy algorithm ...... 49 Fig.3. 8. Fuzzy set for an input variable ...... 53 Fig.3. 9. Structure of artificial neural network ...... 53

Fig.4. 1. Followed methodology for improvement of neural network’s accuracy ...... 61 Fig.4. 2. Different patterns of meteorological parameters (a) Air temperature (b) Relative humidity (c) Diffuse radiation ...... 64 Fig.4. 3. Measured 퐺푆푅 over the period of (1st Jan, 2013 to 31st Dec, 2017) ...... 65 Fig.4. 4. Measured 휃푧 over the period (1st Jan, 2013 to 31st Dec, 2017)...... 66 Fig.4. 5. Change of 휃푧 between two sequent days in (a) April (b) October ...... 67 Fig.4. 6. Change of 휃푧 between two sequent months (a) April and May (b) September and October ...... 67

List of Figures 122

Fig.4. 7. Changing the values of 푅2 and 푅푀푆퐸 over the year with only two terms considered in 휃푧 model ...... 69 Fig.4. 8. Values of 푅푀푆퐸 over the year for different terms considered in 휃푧 model ... 69 Fig.4. 9. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by adapted neural network that has 휃푧 as input parameter ...... 70 Fig.4. 10. Correlation between the predicted and measured 퐺푆푅 for adapted neural network of 휃푧 input parameter over the period (1st Jan, 2013 to 31st Dec, 2017) ...... 71 Fig.4. 11. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by adapted neural network that has 휃푧 as input parameter ...... 71 Fig.4. 12. Average of prediction errors for different values of 훼 ...... 72 Fig.4. 13. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) EWMA (b) WCMA (c) Pro-Energy) ...... 73 Fig.4. 14. Correlation between the predicted and measured 퐺푆푅 for ((a) EWMA (b) WCMA (c) Pro-Energy) over the period (1st Jan, 2013 to 31st Dec, 2017) ...... 74 Fig.4. 15. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) EWMA (b) WCMA (c) Pro-Energy) ...... 75 Fig.4. 16. Harvested energy observations considered for the traditional neural networks (a) NN1 (b) NN2 (c) NN3 ...... 76 Fig.4. 17. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) NN1 (b) NN2 (c) NN3) ...... 77 Fig.4. 18. Correlation between the predicted and measured 퐺푆푅 for ((a) NN1 (b) NN2 (c) NN3) over the period (1st Jan, 2013 to 31st Dec, 2017) ...... 78 Fig.4. 19. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by ((a) NN1 (b) NN2 (c) NN3) ...... 79 Fig.4. 20. Maximum prediction error and execution time versus prediction algorithms ...... 81 Fig.4. 21. Predicted 퐺푆푅 over the period (1st Jan, 2013 to 31st Dec, 2017) by improved neural network of input parameters (휃푧 & Pro Energy results) ...... 82 Fig.4. 22. Correlation between the predicted and measured 퐺푆푅 by improved neural network of input parameters (휃푧 & Pro Energy results) over the period (1st Jan, 2013 to 31st Dec, 2017) ...... 83 Fig.4. 23. Prediction error over the period (1st Jan, 2013 to 31st Dec, 2017) by improved neural network of input parameters (휃푧 & Pro Energy results) ...... 83 Fig.4. 24. Arrangment of neurons and input parameters considered for the simple topology ...... 84

Fig.5. 1. Operating modes or states of neutrality energy management scheme in wireless sensors node ...... 89 Fig.5. 2. Electrical circuit of solar powered sensor node ...... 91 Fig.5. 3. Real hardware of solar powered sensor node ...... 92

List of Figures 123

Fig.5. 4. Predicted energy by different predictive neural networks over the summer week (15th July 2018 to 21st July 2018) ...... 94 Fig.5. 5. Deviation between the actual and predicted values of different prediction algorithm over the summer week (15th July 2018 to 21st July 2018) ...... 94 Fig.5. 6. Prediction error of different predictive neural networks over the summer week (15th July 2018 to 21st July 2018) ...... 95 Fig.5. 7. Distribution of operating states without prediction ...... 96 Fig.5. 8. Distribution of operating states by ((a) EWMA (b) WCMA (c) Pro-Energy) over the summer week (15th July 2018 to 21st July 2018) ...... 97 Fig.5. 9. Distribution of operating states by ((a) NN1 (b) NN2 (c) NN3 (d) Proposed NN) over the summer week (15th July 2018 to 21st July 2018) ...... 98

List of Tables 125

List of Tables

Table 1. 1. Energy sources with corresponding harvesting devices [20] ...... 2

Table 2. 1. Values of 훽퐴 and 훼퐴 for various atmospheric states [79] ...... 23 Table 2. 2. Albedo factor values of variant grounds type [85] ...... 28

Table 3. 1. Activation functions that are used with neural networks ...... 55 Table 3. 2. Performance comparison of different prediction algorithms [73,74,91,92,97– 104] ...... 56

Table 4. 1. Different neural networks for calculating solar energy with their input parameters ...... 62 Table 4. 2. Values of modeling variables for 휃푧 ...... 68 Table 4. 3. Weights of improved neural network ...... 85 Table 4. 4. Biases of improved neural network...... 85

Table 5. 1. Reserved space and processing time for different prediction algorithms utilized in wireless sensor nodes...... 93

Appendices 127

Appendices

Appendices 128

Appendix 1: Components of Pyranometer.

Appendices 129

Appendix 2: Two diode model of solar cell.

푅푠 퐼푃푉 + 퐼 퐼퐷1 퐼퐷2 푠ℎ

푅 퐼푝ℎ 퐷1 퐷2 푠ℎ 푉푃푉

−

Appendices 130

Appendix 3: Effect of different factors on the characteristic curve of solar cell (a) temperature (b) diode factor (c) series resistance (d) parallel resistance.

Current Temperature Current Diode Factor

Voltage Voltage

(a) (b)

Current Current Series Resistance Parallel Resistance

Voltage Voltage

(c) (d)

Declaration of Authorship 132

Declaration of Authorship

I hereby declare that I have written the submitted dissertation for getting the doctorate certificate without any foreign help. No other references were used except the listed ones. The quoted results were always marked with the relevant reference. This dissertation, in the current version, was never submitted for examination either abroad or in Germany.

Erklärung zur Urheberschaft

Hiermit erkläre ich, dass ich die eingereichte Dissertation zum Erhalt des Promotionszeugnisses ohne fremde Hilfe verfasst habe. Es wurden keine anderen Referenzen als die aufgeführten verwendet. Die angegebenen Ergebnisse wurden immer mit dem entsprechenden Verweis gekennzeichnet. Diese Dissertation in der aktuellen Fassung wurde weder im Ausland noch im Inland zur Prüfung eingereicht.

Ph.D. Candidate: Murad AL_Omary

Chemnitz 14 Oct, 2019 Ort und Datum

CV of Author 134

CV of Author

Personal Information

Name Murad Al Omary Date of Birth 9th August 1987 Place of Birth As sarih, Jordan Gender Male Marital Status Married with two Children Nationality Jordanian

Educational Background

Since 10/2017 Chemnitz University of Technology / Germany Electrical Engineering / Institute of Measurements and Sensor Technology. Ph.D. student

06/2010-08/2012 Yarmouk University / Jordan M.Sc. Electrical Power Engineering.  Relevant Courses: Advanced Power Distribution Systems, Advanced Engineering Mathematics, Advanced Power System Protection, Power System Engineering, Restructure of Electric Power Industry.  Thesis Title: “Optimal Design and Analysis of Hybrid Energy Systems for some Study Cases in Jordan”.  G.P.A: 84.3% (very good).

09/2005-06/2010 Yarmouk University / Jordan B.Sc. Electrical Power Engineering.  Relevant Courses: Power Systems Analysis, Design and Erection of Electric Power Systems, Power Systems Protection, Power Systems Distribution, Power Electronics, High voltage Engineering, Transformers and DC Machines, AC Machines, Control Systems Analysis.  Graduation Project: “Speed Control of Induction Motors”.  G.P.A: 77.8% (very good).

09/2004-06/2005 Jordanian High School Examination, Scientific Stream / Ministry of Education.

CV of Author 135

 G.P.A: 87.5% (very good). Work Experience

06/2011-01/2014 Teaching and Research Assistant at German Jordanian University / School of National Resources Engineering and Management.

09/2010-06/2011 Laboratories Instructor at Yarmouk University / Hijjawi Faculty for Engineering Technology / Department of Electrical Power and Machines Engineering.

06/2009-10/2009 Trainee at National Electric Power Company (NEPCO), Jordan.

10/2009-12/2009 Trainee at Irbid District Electrical Company (IDECO), Jordan.

Training and Workshops

10/2008-12/2008 MATLAB and Simulink (30 hours) at Rania Queen Center for Jordanian Studies and Community Service / Yarmouk University.

06/2009-08/2009 Training Courses in the field of electric power system (200 hours), at the Electric Training Center

Skills

Languages  Arabic   English   German 

Software  Good Command with Windows Applications and Internet.  Good Command with Electrical Power Softwares such as (Power World, MATLAB, Multisim, Tina, Circuit Maker, Digsilent Power Factory, NEPLAN, Homer).

Driver’s License Jordanian License.

Membership

Since 07/2010  JEA (Jordan Engineering Association).  IEEE (Institution of Electrical and Electronics Engineering).  JSSR (Jordan Society for Scientific Research).

CV of Author 136

References

 Prof. Olfa Kanoun Position: Head of Institute of Measurements and Sensors Technology / Technical University of Chemnitz. E-Mail : [email protected]

 Prof. Muwaffaq Alomoush Position: Dean of Hijjawi Faculty for Engineering Technology / Yarmouk University. E-Mail : [email protected]

 Prof. Mohammad Abderazzaq Position: Full Professor in the department of electric power Engineering / Hijjawi Faculty for Engineering Technology / Yarmouk University. E-Mail : [email protected]

List of Publications 138

List of Publications

Journals:

 Murad Al Omary, Martin Kaltschmitt and Christian Becker “Electricity System in Jordan: Status and Prospects”, Renewable and Sustainable Energy Reviews; Vol. 81, part 2, Jan 2018, Pages 2398-2409.

 Murad Al Omary, Olfa Kanoun “Energy Consumption Management in Solar Powered Wireless Sensors Using Neural Network Predictive Controller”, Energy Conversion and Management; Submitted.

Conferences:

 Murad Al Omary, Khaoula Hassini, Ahmed Fakhfakh and Olfa Kanoun “Prediction of Energy in Solar Powered Wireless Sensors Using Artificial Neural Network”, 16th International multi-conference on Systems, Signals and Devices, 21-24 Mar, 2019 Istanbul (IEEE Conference Paper).