Urban Wind Map for Delft, Rotterdam and Zoetermeer Fikirte M

Urban Wind Map for Delft, Rotterdam and Zoetermeer Fikirte M. Yemer B.Sc.

12-12-2010

Urban Wind Map for Delft,

Rotterdam and Zoetermeer

Fikirte M. Yemer B.Sc.

Delft University of Technology Faculty of Applied Science Department of Sustainable Energy Technology

DELFT UNIVERSITY OF TECHNOLOGY

DEPARTMENT OF WIND ENERGY

The undersigned hereby certify that they have read and recommend to the faculty of Applied science sustainable energy department for acceptance a thesis entitled “ Urban Wind Map for the Delft, Rotterdam and Zoetermeer’’ by Fikirte M. Yemer B.Sc. in partial fulfilment of the requirements for the degree of Master of Science.

Date ______

Head of Department and Supervisor ______Prof. dr. G.J.W Van Bussel

Reader ______dr. Ir. Wim A. A. M. Bierbooms

Reader ______dr. Hans Zoetelief

SUMMARY Urban wind turbines fall within the scope of the green energy goal of municipalities in the Netherlands. For feasibility and potential green energy contribution assessment of these turbines, realistic wind speed prediction method is essential.

The objective of this thesis is to develop urban wind resource mapping methodology and apply it to Delft, Rotterdam, and Zoetermeer. A non-conventional application of WASP is adapted by treating urban areas as a complex environment. As a topographic map, a Digital surface model that evolves above individual buildings which is then refined to a smooth synthetic surface evolving above cluster of buildings is used. Kriging interpolation and a combination of maximum and moving average grid filtering methods are used to create the synthetic surface. This smooth synthetic surface has lower ruggedness index around the meteorological sites, which indicates a gentle and smooth terrain that is relatively within the WASP working envelop.

Roughness change maps generated by „wasp_map_exe’ and the wind data of Geulhaven and Zestienhoven meteorological stations, collected from the KNMI website are used in this work. The roughness change maps are modified to take into account the standardized potential wind speed of the meteorological sites. Annual mean wind speeds of 5.71 m/s and 4.71 m/s are registered for Geulhaven and Zestienhoven stations respectively. For both meteorological sites total frequency greater than 30 % is registered for south and southwest directions.

Minimum prediction error is observed while using Geulhaven as a source than Zestienhoven. However Zestienhoven predicted the wind speed for Rotterdam with very low percentage error. Furthermore, the wind speed frequency of each predicted site has comparable distributions to that of the respective predictor. A 3.5% difference is seen among the omni directional predicted wind speeds of Rotterdam Noord that are based on the two meteorological sites. The sector wise wind speed differences range from 0.69% to 23%. Frequency difference among predictor sites, Ruggedness index and distance between the predictor and predicted sites contribute to the observed prediction difference.Wind resource maps are developed for the cities of Delft, Rotterdam, and Zoetermeer. The developed resource maps show a logical trend i.e. lower wind speed in the urbanized areas and higher wind speed in areas of low building density. Nonetheless, relatively higher wind speeds are observed at highly elevated locations within the urban areas. These areas have a good potential for installation of UWTs.

It is recommended to use a raw wind speed data so that the introduction of error while modifying the roughness length can be reduced. Introduction of number of masts is very advantageous in refining the input data and validating the wind mapping methodology.

ACKNOWLEDGEMENT

I would like to extend my sincere gratitude to my supervisor Prof. Dr. Gerard van Bussel for his invaluable support during the thesis work. I would like to thank Robert Schneider (CEO) for the opportunity to work with Donqi Urban Windmills.

I would like to acknowledge the stuff members of Map Room of the TUDelft Library for their prompt supply of data; Kasper van Der Heiden, Paul Ten Hoppen, and Tamiru W. Shire for their feedbacks and inputs. I would like to thank the stuff members of the Rotterdam Noord police station for their assistance during my visit to the station.

I would also like to thank Patricia Carrion Gordon and the academic counsellor Mirjam van der Geur for their assistance during the last months of my study.

My special thanks go to Mesi, Fethea, Edi, and G.J. Wiersma for their hospitality during my stay in Delft. G5 it was great to share all the wonderful memories.

Finally yet importantly, I would like to praise my mother for her love care and support. She has been an inspiration throughout my life. Above all, I wish to thank the Almighty God for holding me up and giving me the strength to finalize my study with a good sprit.

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TABLE OF CONTENTS

SUMMARY ...... i

ACKNOWLEDGEMENT ...... iii

TABLE OF CONTENTS ...... v

LIST OF FIGURES ...... ix

LIST OF TABLES ...... xiii

ABBREVIATIONS ...... xv

1. INTRODUCTION ...... 1

1.1. Background ...... 1

1.1.1. Small Wind Turbines in the Netherlands ...... 2

1.1.2. Existing wind Atlases of the Netherlands ...... 3

1.2. Problem Definition ...... 3

1.3. Objective and Scope ...... 3

1.4. Approach ...... 4

1.5. Thesis Outline ...... 4

2. OVERVIEW ...... 5

2.1. Basics of Wind Resource Estimation ...... 5

2.2. Urban Wind Property ...... 8

2.3. Wind Study Methods ...... 11

2.3.1. Use of Onsite Measurement ...... 11

2.3.2. Physical Model ...... 12

2.3.3. Numerical Model ...... 12

2.3.4. Coupled Meso-scale and Micro-scale Modelling ...... 14

2.4. Conclusion ...... 15

3. WASP ...... 17

3.1. Introduction ...... 17

3.2. WASP Sub-Models ...... 19

3.3. Factors affecting the prediction process ...... 24

3.4. Ruggedness Index (RIX) ...... 25

4. SYNTHETIC DIGITAL SURFACE MODEL ...... 27

4.1. Introduction ...... 27

4.1.1. Kriging Interpolation ...... 28

4.1.1.1. Steps of kriging interpolation ...... 29

4.1.2. Grid Filtering ...... 30

4.2. DSM Application Software ...... 31

4.3. Elevation Data ...... 31

4.4. Construction of a synthetic surface above the urban area ...... 34

5. WIND SPEED DATA ...... 43

5.1. Historical Wind Data ...... 43

5.1.1. Station 344: Zestienhoven ...... 44

5.1.2. Station 343 Rotterdam Geulhaven ...... 45

5.1.3. Comparison of Zestienhoven and Geulhaven ...... 46

5.2. Short Term Wind Data ...... 48

5.3. Wind Speed Correlation ...... 50

6. APPLICATION OF WASP ANALYSIS ...... 51

6.1. Cross prediction ...... 51

Summary of the Cross Prediction ...... 64

6.3. Ruggedness index ...... 67

6.4. Effect of Contour level ...... 67

7. RESULTS AND DISCUSSIONS ...... 69

Introduction ...... 69

Rotterdam ...... 69

Delft ...... 71

Zoetermeer ...... 73

8. CONCLUSIONS AND RECOMMENDATIONS ...... 75

8.1. Conclusion ...... 75

8.2. Recommendation ...... 76

REFERENCES ...... 77

APPENDICES ...... 81

Appendix A ...... 81

Appendix B ...... 82

Appendix C ...... 86

Appendix D ...... 87

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viii

LIST OF FIGURES

Figure 1: Diffuser augmented Donqi wind turbine[5] ...... 2 Figure 2: Neutral Atmospheric Boundary layer shear Profile[10] ...... 5 Figure 3: Step change in surface roughness incorporates a changing boundary layer [15] ...... 8 Figure 4 : Results of a CFD calculation of a rectangular building in skewed flow[16] ...... 9 Figure 5: Urban wind regimes showing disturbed regions around simple buildings[18] ...... 9 Figure 6 : vortex formation (left) and 3D vortex along the bordering buildings (right) [15] ...... 10 Figure 7 : Funnelling of parallel approaching wind in a canyon [15] ...... 10 Figure 8: Setup to generate an atmospheric Boundary layer in a wind tunnel[15] ...... 12 Figure 9 : Alaiz test site used for comparison of WASP and FLUENT[26] ...... 14 Figure 10: The wind Atlas methodology [31] ...... 18 Figure 11: The Zooming grid of BZ flow model ...... 20 Figure 12: Ruggedness index of area around Rotterdam Noord (the thick red Points inside the circle indicate RIX greater than 0.3) ...... 25 Figure 13: Difference between DEM and DSM [39] ...... 27 Figure 14: Variogram Model for Sample Data with scale of 6000 and length of 480 ...... 30 Figure 15: A 3 by 3-filter size grid showing the neighbouring (green) and corresponding (red) grid nodes ...... 31 Figure 16: Location of the Netherlands in RD coordinate [54] ...... 33 Figure 17: AHN data cell representation on RD coordinate system ...... 34 Figure 18: DSM (Top) and shaded Relief map (Bottom) of the grid where kriging interpolation is applied ...... 36 Figure 19: DSM of filtered grid (Maximum filter size 5 by 5) ...... 37 Figure 20: 3D model of the double filtered grid ...... 38 Figure 21: Overlaid 3D surface maps of double filtered (grey) and single filterd grid(red) ...... 39 Figure 22: 3D surface of the final Grid ...... 39 Figure 23: Elevation contour of the final grid (synthetic surface) ...... 40 Figure 24 : Observed Wind Climate of Zestienhoven ...... 44 Figure 25: Observed Wind Climate of Geulhaven ...... 45 Figure 26: Sector wise wind speed frequencies of the KNMI stations ...... 47 Figure 27: Blockage effect of the neighbouring areas for Geulhaven [Google earth] ...... 47 Figure 28: Old measurement setup (left) and new measurement setup (right) of Rotterdam Noord Police station ...... 48

Figure 29 : Observed Wind Climate of Rotterdam Noord Police station ...... 49 Figure 30: AHN data used for creating the DSM used in cross prediction ...... 51 Figure 31: Wind Atlas for R-class 0 at 10 m ...... 52 Figure 32: Wind atlas trend lines (wind shear) for different roughness classes ...... 53 Figure 33: Self-Predicted wind climate of Zestienhoven ...... 54 Figure 34 : Wind Speed Frequency difference ...... 56 Figure 35: Frequency difference of Observed and Predicted wind climate of Geulhaven and the Predictor site Zestienhoven ...... 56 Figure 36 : Wind Speed Frequency difference of observed and predicted wind climate of Rotterdam Noord station ...... 58 Figure 37: Frequency distributions of observed, predicted wind climates of Rotterdam Noord and Zestienhoven (predictor) ...... 58 Figure 38: wind Atlas of Station Geulhaven for R-class 0 and elevation 10 m ...... 59 Figure 39: Self predicted wind speed of Geulhaven ...... 60 Figure 40: Frequency difference of predicted and observed wind climate of Zestienhoven ...... 60 Figure 41: Frequency distributions of observed and predicted wind climate of Zestienhoven and the predictor site ...... 61 Figure 42: Frequency difference predicted and OWC (Rotterdam Noord Police station) ...... 63 Figure 43: Frequency distribution of Observed and predicted wind climate of Rotterdam Noord and the Predictor site Geulhaven ...... 63 Figure 44: Relation between the prediction differences and Frequency difference of the predicted sites ...... 66 Figure 45: Wind speed prediction error versus ...... 67 Figure 46 : Variation of omni directional prediction error with contour level ...... 68 Figure 47: Effect of contour level on the Sector wise predictions (Rotterdam Noord) using Geulhaven as predictor ...... 68 Figure 48: Wind map for part of Rotterdam ...... 70 Figure 49 : Shaded relief map of part of Rotterdam for which the wind map was developed ...... 70 Figure 50: 25 m resolution wind map for Part of Delft ...... 71 Figure 51: Shaded Relief map of part delft for which the wind map was developed ...... 72 Figure 52:25 m resolution wind map for part of Zoetermeer ...... 73 Figure 53: shaded relief map for the area on which the map was developed ...... 74 Figure 54 : Synthetic surface above area of Rotterdam ...... 82 Figure 55: Snap shot of Rotterdam [source Google earth] ...... 83 x

Figure 56 : Synthetic surface evolving above area of Delft ...... 84 Figure 57: Snap shot of Delft [Google Earth] ...... 84 Figure 58:3D synthetic surface evolving above the areas of Zoetermeer ...... 85 Figure 59: Snap shot of Zoetermeer [Google earth] ...... 85 Figure 60 : A 100 m resolution Wind map for Rotterdam ...... 88 Figure 61: A 25 m resolution Wind map of Rotterdam 1 ...... 89 Figure 62: A 25 m resolution Wind map for Rotterdam 2 ...... 90 Figure 63 : A 25 m resolution Wind map for Rotterdam 3 ...... 91 Figure 64: A 25 m resolution Wind map for Rotterdam 4 ...... 92 Figure 65 : A 25 m Resolution wind map of Zoetermeer ...... 93

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LIST OF TABLES

Table 1: Standard roughness class used in WASP [31] ...... 22 Table 2 : Roughness length values depending on density of package and height of buildings[17] 23 Table 3: AHN data sets used for data Request ...... 32 Table 4: Summary of Ruggedness Index for Geulhaven and Zestienhoven ...... 41 Table 5: Observed wind climate comparison of the two KNMI stations ...... 46 Table 6: Summary of the Observed Wind Climates ...... 49 Table 7: Zero time lag Correlation between the KNMI stations ...... 50 Table 8: Summary of wind atlas of Zestienhoven for different elevations and roughness classes . 53 Table 9: Difference between predicted and observed wind Climates of Geulhaven ...... 55 Table 10: Difference between predicted and observed wind climates of Rotterdam Noord Police Station ...... 57 Table 11 : Summary of Wind atlas data for Geulhaven ...... 59 Table 12 : Difference between predicted and observed wind climate of Zestienhoven ...... 61 Table 13: Difference between predicted and observed wind climate of Rotterdam Noord station 62 Table 14: Cross prediction results ...... 64 Table 15: Summary of statistics of cross predictions ...... 64 Table 16: Prediction similarity in using Geulhaven and Zestienhoven as predictor ...... 65 Table 17 : Land-use and roughness classes in LGN3+ used by „wasp_map.exe‟ ...... 86

xiii

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ABBREVIATIONS ABL: Atmospheric Boundary Layer

AHN: Actual Height of the Netherlands

AMS: American Meteorology Society

BLUE: Best linear unbiased estimator

CFD: Computational Fluid Dynamics

DSM: Digital Surface Model

DXF: Drawing Exchange Format

EFRO (ERDF): Europees Fonds Voor Regionale Ontwikkeling (European Regional Development fund)

EIA: Energy Investment Deduction

IBL: Internal Boundary layer

KAMM: Karlsruhe Atmospheric Mesoscale Model

KNMI: Koninklijk Nederlands Meteorologisch Instituut (Royal Netherlands Meteorological Institute)

LIDAR: Light Detection and Ranging

MCP: Measure Correlate and Predict

MEP: Milieukwaliteit Van de Elektricites Productie (Electricity generation Environment Quality)

OWC: Observed Wind climate

P.E: percentage Error

PBL: Planetary Boundary Layer

PWC: Predicted Wind Climate

RADAR: Radio Detection and Ranging

RANS: Reynolds Averaged Navier Stokes Equation

RD: Rijksdriehoekscoördinaten (National Triangular coordinates)

RIX: ruggedness index

SRTM: Shuttle Radar Topography Mission

TIFF: Tagged Image File Format

USGS: United States Geological Survey

UWT: Urban Wind Turbine

W.P.D.: Wind power Density

WASP: Wind Atlas Analysis and Application Program

WRF: Weather Research and Forecasting xv

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CHAPTER ONE

1. INTRODUCTION

This chapter gives a brief description about the application of urban wind turbines, their history in the Netherlands and the suitability of the already existing wind maps of the Netherlands for urban wind energy deployment. It also states the research objectives and thesis structure.

1.1. Background Until recently the main renewable energy sources explored in an urban environment are solar energy and heat pumps [1]. Urban wind turbines or Small Wind Turbines are defined as turbines that are specially designed for built in environment, and can be installed on buildings or on the ground next to buildings [2]. The potential of these turbines was undermined by improper site selection. The accompanying technical challenges such as excessive noise, turbine under performance and low power density and in some extreme cases cracking of turbine blades lead to infant mortality of Small wind turbine applications. In addition to the problems listed above, installation of wind turbines which are not specifically designed for urban wind conditions harmed the reputation of using wind turbines in urban areas[3].

Nevertheless, in the last few years small wind turbines that are designed to withstand the complex wind behaviour of an urban environment began to emerge. Some of these wind turbines are even installed in urban areas. Urban wind turbines (UWTs) have a potential to harness the wind in cities and towns where space for installation of large wind turbines is not available or large wind turbines can significantly damage the city outlook [2]. The introduction of UWTs can contribute to the diversity of national power generation and renewable energy commitment at a household level. Moreover, depending on their penetration level they may also reduce transmission losses in the local grid [4]. Their economic viability varies from place to place and depends on the future developments of electricity market such as government policy on subsides and international commitment on renewable energy shares.

The Wind Energy Integration in the Urban Environment (WINEUR project) has conducted a study on the deployability of micro wind turbines in the built-in environment [2].The study presented the current challenges that undermine the use of micro wind turbines into the following different aspects; turbine technology, development costs, public awareness and construction permits.

CHAPTER ONE

1.1.1. Small Wind Turbines in the Netherlands The application of small wind turbines in the Netherlands gave birth when Nuon, the Dutch utility company, presented Tulipo at the roof of the Dutch pavilion at the Hannover Fair Expo 2000. Tulipo‟s market success inspired other producers such as Turby, Energy Ball and WindWall for a market share which resulted in other new types of UWTs [1]. However, the design and technology of UWTs is not yet matured and it had not been proven in the field.

DONQI Urban Windmill is one of the youngest companies that introduced new designs of UWTs with the aim of increasing penetration level of decentralized generation. The Donqi Turbine uses an innovative technology with built-in Venturi noise dampener, thus produces high yield while operating safe and quiet[5].

Figure 1: Diffuser augmented Donqi wind turbine[5] One interesting fact in the Netherlands is the lively connection existing among environmental departments of the municipalities which allow them to share experiences about the application of UWTs[1]. Financial supports such as Energy Investment Deduction (E.I.A) and Electricity Generation Environment Quality (M.E.P) are made available for commercial organisations .Some provinces and municipalities also provide other subsidies as part of their renewable energy development programmes [2].

Despite the remarkable improvements in UWTs technology, lack of full understanding of the wind behaviour in urban environment and its accurate prediction made UWT applications challenging [3].

CHAPTER ONE

1.1.2. Existing wind Atlases of the Netherlands

The Wind Atlas of the Netherlands is included in the European wind atlas for five different topographic conditions at an altitude of 50 m [6]. It shows the regional wind climate discarding the influences of micro-scale topography. In 2005, KEMA Netherlands BV developed another wind atlas for all the provinces at an elevation of 100 m [7]. It is developed to support policy makers of large wind projects in the governments, provinces, and municipalities. The wind map used wind data from KNMI HYDRA project, and WASP computer program. It is prepared for a grid area of 200 m by 200 m. As the aim of this wind map is for large wind turbine developments, only wind speeds at an elevation 80-100 m can be deduced from it.

1.2. Problem Definition The application of UWTs necessitates Urban wind map detailed with the effect of micro-scale features. The wind Atlases mentioned in Section 1.1.2 do not include micro-scale features of an environment. They also have low resolution. Hence, they are not able to provide enough information for urban wind turbine application.

1.3. Objective and Scope Having an urban wind map will be advantageous in identifying suitable candidate sites of high wind energy concentration to be used for further verification through measurements and for installation of UWTs. The wind behaviour in urban environments is very complex. A large-scale wind study of urban areas using conventional methods is very difficult. The main objective of this project is to develop an urban wind mapping methodology and apply it to the cities of Delft, Rotterdam, and Zoetermeer.

The work is carried on in cooperation with Donqi and TUDelft Wind Energy Section, and is supported by the Europees Fonds Voor Regionale Ontwikkeling (EFRO) also known as European Regional Development fund (ERDF) subsidy scheme.

CHAPTER ONE

1.4. Approach In this study, a different approach of using the conventional wind resource assessment method, WASP, is followed. It is based on the assumption that urban areas can be treated as a complex environment in which „skimming flow‟ takes place. For doing so, a synthetic topography that evolves above cluster of buildings and other features of the area is developed using the Actual Height of the Netherlands (AHN) data. In the synthetic topography, higher elevation areas that resulted from skyscrapers and high buildings are assumed as mountains and the lower elevation areas as valleys. The effect of density and height variation of buildings is taken into account by assigning different roughness length values. For classifying the area, Google Earth and the DSM developed from the AHN data are used. As the blanket like synthetic surface is above buildings and other features of an urban area, no obstacle model is used. The methodology used in this work is adapted from a study made in Portugal, Torres Vedras city [8].

1.5. Thesis Outline The report covers seven main sections and is organized as follows: First, Chapter two covers a literature review about the basics of wind resource study and urban wind properties. The current wind study methods and their applicability to complex and urban environments are also summarized briefly. In the next section, Chapter three, the Wind atlas methodology (WASP) and its sub models are discussed in detail. Chapter four then addresses the construction of synthetic surface to be used as a topographic map. In Chapter five, the long term and short-term wind data used for this study are presented. Chapter six then gives the result of WASP analysis. Chapter seven discusses the wind maps developed for Delft, Rotterdam, and Zoetermeer. Chapter eight then gives the conclusions and recommendations of the study.

CHAPTER TWO

2. OVERVIEW Wind characteristics of a particular site can be best expressed using three key parameters; Annual Mean wind speed, Wind frequency distribution and wind direction. This chapter introduces atmospheric boundary layer, wind shear, and roughness in addition to the basic wind resource characteristics aforementioned. It also discusses the different wind study methods that are currently in use and their applicability to urban environment.

2.1. Basics of Wind Resource Estimation While performing wind resource estimation a good knowledge about the annual mean wind speed, frequency, and wind direction distribution are very important. Atmospheric boundary layer, Wind shear, and Roughness length are also necessary for profound study of the wind property at a certain site. A brief introduction to these wind properties is presented as follows.

Atmospheric Boundary Layer

Atmospheric Boundary layer (ABL) or Planetary Boundary Layer (PBL) is part of the troposphere that is directly influenced by the presence of the earth‟s surface and responds to surface forces within a time scale of an hour or less [9]. The depth of ABL ranges from tens of meters in strongly statically stable situations, to several kilometres in convective conditions over deserts and varies with atmospheric conditions and time of the day [9]. It has two main layers: Inner (surface) layer and Outer (Ekman) layer. Coriolis forces dominate the outer layer and are negligible in the inner layer. The inner layer constitutes about 10% of the ABL depth adjacent to the ground and is affected by surface roughness[10].

Figure 2: Neutral Atmospheric Boundary layer shear Profile[10]

CHAPTER TWO

Figure 2 shows schematics of wind shear profile within the ABL. Wind shear is the horizontal wind speed increase with elevation. It directly affects the power available at different wind turbine hub heights and cyclic loading of the blades[11]. It depends on the wind speed, wind direction, height above the ground, ground surface roughness variation, atmospheric stability and nature of terrain [12]. Its dependency on these entire factors weighs up and makes wind shear modelling difficult. Usually, it is modelled using the logarithmic wind profile which is based on principles of boundary layer flow Equation 1; and the empirically developed power law model Equation 2. The log law is applicable only to the inner layer of ABL.

Equation 1

Where : is the roughness length

: and are the elevations where wind speeds and are measured

( ) Equation 2

Where : and are wind speeds measured at and respectively : is the power law exponent In Equation 1, roughness length that shows the effect of roughness of a terrain on the wind profile is included. It is defined as the height above the Zero displacement plane1 at which the mean wind becomes zero when extrapolating the logarithmic wind speed profile downward through the surface layer[9]. Frequency distribution (probability distribution Profile) Probability distribution of wind speeds shows the range of wind speed and its frequency of occurrence. The weibull probability density function, Equation 3, gives a very good representation of wind speed frequency for many areas.

( ) ( ) ( ⁄ ) Equation 3

Where ( )is the probability of occurrence of wind speed „U‟ : „a‟ [m/s2] is the weibull scale factor : is the shape factor that describes the distribution of the wind speeds (how peak the curve is)

1 Zero-plane displacement is the height in meters above the ground at which zero wind speed is achieved because 2 of flow obstacles such as trees or buildings. It is generally approximated as /3 of the average height of the obstacles.[9] (2000, The American Meteorological Society's (AMS) Glossary of Meteorology (2nd ed.). Available: http://amsglossary.allenpress.com/glossary 6

CHAPTER TWO

The weibull scale factor „a‟ and shape factor „k‟ are related to the average wind speed with, Equation 4.

Equation 4 ( ⁄ )

Where is the mean wind speed

( ) is the gamma fnction

Sometimes sites with different summer and winter wind climates can be represented by double peaked Weibull distribution. A special case of the probability density function called Rayleigh distribution occurs when the shape factor k equals two. Equation 4 will then be simplified to Equation 5.

Equation 5

Annual Mean Wind speed Annual Mean Wind speed as the name indicates is the average wind speed of a certain site over a year. It is useful in giving an estimate of the average available energy. Since the available energy varies as the cube of the wind speed, a very accurate estimate of wind speed is crucial. It is worth mentioning that sites with the same mean wind speed but dissimilar frequency distribution may have different energy potential.

Power Density The power density of the site is the average wind power over one square meter of a turbine; it can be calculated using individual wind measurements Equation 6. In [13], it is indicated that for a wind distribution with a shape factor k=2, if the estimate of the power density is done using annual mean wind speeds underproduction of annual power density by a factor of 1.9 will be observed.

∑ Equation 6

Where : ⁄ is the power density : is the number of wind records th : ⁄ is the i wind speed : ⁄ is the air density

CHAPTER TWO

Wind Direction

The other essential wind property to be studied while undertaking a wind resource assessment is wind direction. It is represented using a wind rose, which shows the frequency of occurrence of a wind at a certain direction. Having knowledge of the prevailing wind direction will help make an informed decision during the installation of the wind turbine for maximum power production.

2.2. Urban Wind Property Wind regime in the built environment is generally characterized by the low annual mean wind speed and high turbulence intensity [14]. When wind flows from an open area to a built-up area, the inner ABL evolves to the local building density. The wind speed decreases due to increased roughness caused by physical obstacles Figure 3. The wind speed reduction increases when the wind flows from sub urban areas to a more built up areas (like a city centre with high buildings). The roughness increase also causes higher turbulence intensity and directional variability.

Figure 3: Step change in surface roughness incorporates a changing boundary layer [15]

When a wind flows around a building separation occurs and bubbles form at the leading edge and sides of the building Figure 4. Mertens performed a CFD simulation to visualize the flow around a single rectangular building [16]. It was found out that above the separating stream there is up to 30% increase in total wind speed. It was also seen that there is a velocity gradient above the roof. Based on his study he also suggested some guidelines on the installation height of wind turbines on rooftops.

When more than one building is involved, the wind property varies with type, arrangement, and density of the buildings found in the area. As cited in [17-18] using a wind tunnel experiment three different types of flow were demonstrated depending on the density of packing of the obstacles Figure 5:

CHAPTER TWO

1. Isolated flow in which elements are far apart and act as individual wake generators, 2. Wake interference flow in which the spacing is close enough that the wakes reinforce each other, and 3. Skimming flow in which there is high density of packing and hence the main flow skips over the top of elements.

Figure 4 : Results of a CFD calculation of a rectangular building in skewed flow[16]

Figure 5: Urban wind regimes showing disturbed regions around simple buildings[18] In [17] it was indicated that arrays of elements with similar heights are less „rough‟ than one with variable heights, even when spatially averaged mean heights are the same. Moreover, buildings that are taller than their surroundings have higher wind speed at their rooftops because the wind is less affected by the internal urban boundary layer which is very much affected by the surface roughness [17].

CHAPTER TWO

An urban canopy is the assemblage of buildings, trees, and other objects composing a town or city and the spaces between them [9]. A canopy model has been developed to study the effect of built up area on wind speed. By applying the model to the city of Los Angeles; it was shown that there is an increase in wind speed at lower heights (less than 5 m) of the canopy [19].

Urban (street) canyon, which is the characteristic geometry formed by a city street and its flanking buildings [9], is a type of an urban area in which distinct flow types could be seen depending on the direction of the wind flow. In [15] it was explained that If the flow is perpendicular to the street axis a vortex develops in the middle of the canyon, and a three dimensional vortex moves along the street Figure 6. If the wind is however approaching parallel to the street canyon the buildings funnel it Figure 7. Furthermore, high roughness is observed if the flow is normal to the street axis than it is parallel[17].

Figure 6 : vortex formation (left) and 3D vortex along the bordering buildings (right) [15]

Figure 7 : Funnelling of parallel approaching wind in a canyon [15]

CHAPTER TWO

2.3. Wind Study Methods Nowadays, most urban wind studies cover feasibility and application of UWTs besides investigating the practical problems such as wind force on structures, pedestrian comfort, and air pollution. In wind energy applications, overestimation of wind speed can lead to an economic loss and underestimation may leave places of high potential undiscovered. Thus, the need for accurate wind forecast methods is evident. Different methods have been developed and are in use for wind resource assessments; namely, physical model, on site measurements and Numerical models. The wind resource study methods and their applicability to complex and urban environments are discussed in the following sections. 2.3.1. Use of Onsite Measurement On site measurement is a method that uses measurement masts installed at the area of interest for a certain period. The cup and Ultrasonic anemometers are the most commonly used measuring instruments. The cup anemometers measure average speed and can be coupled with wind vane for wind direction detection. They provide limited information on turbulence and vertical wind component, but they are reasonably accurate and inexpensive [3]. Ultrasonic anemometers measure instantaneous wind speed and wind direction. Unlike Cup anemometer, it can sufficiently detect turbulence. Though it requires higher fixed cost compared to Cup anemometer it has lower operational costs as it has no moving parts[3]. When there are no long-term wind data at a target site, Measure Correlate and Predict (MCP) can be used to correlate the short-term measured data to long-term reference data. It statistically correlates the short-term on-site wind measurements to a reference weather station to obtain long-term wind data for the site[20] . For this method, three sets of data: on-site measured wind data, concurrent measured data at the reference site, and Long-term wind data at the reference site are needed. The concurrent data sets can be used to create the correlation and the long-term reference data with the correlation can be used for obtaining long-term wind data of the site. The measuring mast can be arranged to a hub height of the proposed wind turbine to avoid wind shear effect [14]. Wind data need to be collected at least for one full year to grasp firmly the daily and seasonal variations. This method gives a good accuracy for resource assessment in an installation area. Nonetheless, it may also lead to higher costs expensive than turbine cost, due to the tedious data collection and analysis work [8]. Construction of wind maps through extrapolation of these local data may lead to errors as the data highly depend on roughness and topography.

CHAPTER TWO

2.3.2. Physical Model

Physical models use downscaled models of buildings and other features of the area and an atmospheric boundary layer wind tunnel. The model is placed in a wind tunnel, and the boundary layer upstream of it is created using vortex generators Figure 8. Measurements at different points are taken and adjusted to full scale by keeping the Reynolds number constant [15]. The main advantage of a physical model is its reproducible environment with an overall control over the individual parameters. This allows studying the effect of variation of specific parameters on the overall performance of the model, making it suitable for planning of urban environment [21]. However, it is expensive to construct a 3D physical model and a wind tunnel with the possibility of simulating stratified atmosphere [8].

Figure 8: Setup to generate an atmospheric Boundary layer in a wind tunnel[15] 2.3.3. Numerical Model

Numerical Models are methods that use computers to perform large number of calculations of fluid flow and simulations of flow pattern and fluid structure [21]. Different types of these models ranging from simple linear models to more complex non-linear models are available for wind profile study. Linear models have limited accuracy and high resolution with low computational time and ease of application while complex non Linear models have higher accuracy at the expense of extensive computational effort [21].

Wind Atlas Analysis and Application Program (WASP) that is developed by Risø Laboratory, Denmark in 1987 is one of the most widely used Linear Numerical Models. It is limited to a neutrally stable wind flow over low smooth hills with attached flow. It converges well in an area of simple terrain but underperforms flow separation and recirculation [21]. Due to the long

CHAPTER TWO familiarity with this model, empirical adjustments such as Ruggedness Index 2(RIX) are available to improve its results in a complex terrain[22]. The working principle, sub models of WASP, and its applicability in complex environment and correction factor used for improving the results are discussed in Chapter 3.

Computational Fluid Dynamics (CFD) is a non-linear numerical method. It is a technique that solves numerical fluid models using the Reynolds Averaged Navier Stokes (RANS) equations. ANSYS-CFX and FLUENT are among the commercial CFD software packages used to determine the wind behaviour in highly complex terrains. The other CFD based software is WindSim that combines advanced numeric processing with compelling 3D visualization in a user-friendly interface [21]. In this software, the k- ε turbulence model is used[23]. 3DWind is non-linear Navier-Stokes solver which uses the 3D finite volume method which divides the solution domain into finite number of grids and the RANS to describe the interaction between the grids[22]. Generally, CFD modelling is computationally expensive and very difficult to extend the simulation domain to cover a large area such as cities.

Performance comparison of WASP and WindSim on a complex terrain was performed in Scotland. The comparison takes into account overall wind speed, sector wind speed, and wind direction prediction with measurement data from South West Scotland as a baseline for validation. WASP has shown superior accuracy in predicting sector wind speeds and wind direction whilst WindSim predicted the overall wind speed with a better accuracy. However, the introduction of RIX improved the accuracy of WASP significantly and thus gave it an overall superiority in accuracy[24]. Moreover, a study in Norway showed that despite the complex terrain the model was applied, WASP performed better than WindSim and 3DWind models for vertical wind profiles, and annual average wind speed estimation[25] .The study also mentioned that the CFD models are advantageous because of the explicit calculation of Turbulence.

Another comparison (between linear (WASP) and non-linear (FLUENT 6.2) wind flow models) was made for a 4 km long Alaiz hill, North of Spain[26]. The study has shown that Fluent 6.2 predicts wind speed with higher accuracy than (WASP) based on measurement data on the same hill.

2 RIX is defined as the percentage of terrain within a given area that exceeds a 30% slope .It is used to take in to account flow separation when WASP is used in a complex terrain 13

CHAPTER TWO

Figure 9 : Alaiz test site used for comparison of WASP and FLUENT[26] 2.3.4. Coupled Meso-scale and Micro-scale Modelling

Meso-scale models make wind prediction for larger regions. Though they can give an overview of wind speed for an entire area, they cannot be used for the siting purposes as they have big grid resolution and do not include the local topographical effects. The widely used Meso-scale wind models are KAMM (Karlsruhe Atmospheric Meso-scale Model) and WRF (Weather Research and Forecasting). The micro scale wind models include the effects of topographical features such as obstacles; orography and terrain roughness. They also have small grid resolution, which makes them suitable for siting purposes. The most widely used micro-scale models include CFD models, and WASP.

In Norway, the coupling of micro-scale model with meso-scale model was performed and validated for three wind farms in regions of different terrains[27]. The meso-scale model (WRF) was used to create a wind map with a resolution of 2 km from the meteorological stations. Another study [28] performed by combining MMKK with WASP also concluded that the coupled method improves the accuracy of prediction .

WRF/CFD coupled with environmental library model was tested for Oklahoma City to get an improved high-resolution wind characterization of an urban area as well as improved meso-scale data. The model was then found to be accurate and efficient [29].

CHAPTER TWO

2.4. Conclusion As discussed in Section 2.2 urban areas are composed of buildings, canopies, street canyons etc. each with distinctive wind behaviours. Different models can be (and have been) developed with certain accuracy to study wind behaviour for the different types of urban areas. However, when a large-scale wind study such as wind resource mapping is considered the complexity of the urban wind behaviour makes it difficult.

Of the different wind study methods introduced in Section 2.3, CFD model FLUENT gave high accuracy in complex environment. The main limitation to this model is its being computationally expensive and having small computational domain that makes it difficult to do wind mapping in a larger scale. Despite its limitation in complex terrain, WASP gave comparable (sometimes better) results to the CFD models WindSim and 3DWind, when used in complex terrains of Scotland and Norway. Application of ∆RIX also improved its accuracy. However, this cannot prove that applying WASP in complex environment gives a good result.

It is also seen that Coupling of micro-scale and meso-scale models improves wind prediction accuracy. However, no comparison studies are made to indicate which coupled methods WRF/CFD, KAMM/WASP or WRF/WASP performs better in urban area.

Even though WASP has some limitations, such as lower accuracy in a complex environment and no turbulence intensity analysis, it was adapted for this work because of the following reasons

 Widely used and accepted method even  Less computational time when compared to CFD models  Covers larger domain for wind mapping purpose  Gives high resolution wind map

CHAPTER TWO

CHAPTER THREE

3. WASP

In this chapter, the working principle and sub models of WASP are discussed. The limitation of WASP in a complex terrain and the error indicator RIX are also covered.

3.1. Introduction WASP is based on the concept of linearized flow model. In this flow model, flow over a complex terrain is viewed as the sum main logarithmic flow over flat terrain and uniform roughness and field of perturbations caused by the departure from the flat terrain. The set of steady state equations deduced from mass and momentum flows are linearized and solved by assuming that these perturbations are small compared to the main flow [30].

The working procedure is based on the use of set models to do some correction on measured wind data and then analysis of the corrected data in terms of frequency distribution. The steps are explained as follows and are seen in Figure 10.

1. Analyse the observed wind climate from the measurement site and create a wind rose and wind distribution:- Time-series of wind speed and direction —> observed wind climate (OWC) 2. Remove the local effect such as roughness, terrain and obstacles using the different sub models and extrapolate it vertically to create a regional wind atlas over a flat and homogenous terrain and neutral atmosphere at a range of heights and roughness conditions (up arrow):- Observed wind climate + site description —> regional wind climate (wind atlas data sets) 3. Apply local effect of the predicted site using the sub models again and create a local wind climate (down arrow) to get a „Predicted wind Climate‟. For wind mapping purpose, this step can be applied on number of grid cells. Regional wind climate+ site description Predicted Wind Climate (PWC)

CHAPTER THREE

Figure 10: The wind Atlas methodology [31]

CHAPTER THREE

Weibull Fitting

While analysing the observed wind climate weibull fitting is the most important step. Usually the observed wind climates deviate from the weibull distribution .In this case choosing an appropriate weibull fitting method is very important. In WASP, moment fitting method is used [31]. In this fitting method for each sector, the two-weibull parameters are determined so that the following two conditions are met

 The total wind energy in the fitted weibull distribution and the observed distribution are equal and  The frequencies of occurrence of the wind speeds higher than the observed average speed are the same for both distributions.

3.2. WASP Sub-Models A wind profile near the surface of the earth is influenced by the topography. For wind power meteorology, these effects can be summarized into three main categories [32].

 An orographic element when the scale of terrain features becomes larger than the height of the point of interest.  A roughness element when the collective effect of terrain surface and its roughness lead to the retardation of the wind near the ground.  An obstacle element when the terrain feature (obstacle) has comparable height to the point of interest and is located close to the point of interest.

In WASP orographic, roughness change, and shelter models are used to address the above effects of topography on wind profile.

The Orographic Model

The orographic sub model is used to calculate the wind velocity perturbations induced by orographic features such as single hills or complex terrain. For doing so, WASP utilizes a „BZ model‟. During the analysis, it first calculates the potential flow perturbation induced by the terrain and then modifies it to take into account the effects of surface friction in the inner layer. The detailed mathematical formulas used in this model can be found from [31].

CHAPTER THREE

Figure 11 shows the Zooming grid of the BZ model. While performing wind prediction for the hill site it uses all the grid shown [33] .The gird is zoomed from 25 by 25 sq. km area in one to 200 by 200 sq. km in four. A BZ flow model has the following advantages [33] .

 It employs a high-resolution Fourier-Bessel expansion, zooming, polar grid. This is coupled with a map analysis routine in order to calculate the potential flow perturbation profile at the central point of the model.  It integrates the roughness conditions of the terrain surface into the spectral or scale decomposition. The 'inner-layer' structure is calculated using a balance condition between surface stress, advection, and the pressure gradient.  It uses an atmospheric boundary layer thickness of approx. 1 km to force the large-scale (say, more than a few kilometres) flow around high-elevation areas.

1 2

3 4 Figure 11: The Zooming grid of BZ flow model

CHAPTER THREE

Roughness change model

The roughness change model is used to address the influence of the surface roughness around the site. When wind flows from an area of roughness length to the internal boundary layer grows to a certain height „h‟ downwind from the roughness change [31]. Equation 7 can be used find the height of the IBL at a distance „x‟ from the roughness change.

( ) Equation 7

Where ( ) : h is the boundary layer height : x is the distance from roughness change By matching the neutral wind profile at height „h‟ an empirical relation that models the friction velocity was developed Equation 8 [31]. This equation is used to relate the friction velocity at the observed site to the friction velocity upstream of the roughness change.

( ⁄ ) Equation 8 ( ⁄ )

Where: is the friction velocity upstream of the roughness change

: is the friction velocity downstream the roughness change

Upstream/downstream from the roughness change and above the developing IBL the wind speed is determined using logarithmic wind profile and the upstream/downstream roughness lengths. The friction velocity at the point of interest can be determined using Equation 8 . Below this height and downstream the roughness change however, the wind profile is perturbed and it is impossible to use logarithmic wind profile. It is modified by taking the height above the ground, the two roughness lengths, and the height „h‟ of IBL in to consideration [31]. Numerical modelling and experimental evidence proved that the three logarithmic parts in Equation 9 model perturbed wind profile very well[31].

CHAPTER THREE

( ⁄ )

( ) Equation 9 ( ⁄ ) ( ⁄ )

{ ( ⁄ )

Where ( ⁄ ) and

( ⁄ ) and

is the von Karman constant

are the friction velocity and roughness length corresponding to the measured wind speed respectively

: are the friction velocity and roughness length upstream : s the height where wind speed prediction is to be done

The average surface stress and surface wind speed depends on the surface conditions only up to a certain upstream distance. While using roughness change map it is advisable to consider surface conditions up to 10 km. However if there is a considerable roughness change such as coastal area extending the map up to 20 km will improve results [32]. The roughness length is dependent on the size and distribution of elements such as vegetation, built up areas it changes with foliation, snow cover etc., and hence is treated as a climatological parameter.

For generating the wind atlas, WASP uses different standard roughness classes shown in Table 1 .

Roughness class Roughness length (m) Terrain description

0 0.0002 Smooth land and water surfaces 1 0.03 Farmland with very few buildings/trees 2 0.1 Less open farmland with trees and buildings 3 0.4 Rough surface with trees and buildings Rough surface with dense trees and buildings(urban 4 1.5 areas)

Table 1: Standard roughness class used in WASP [31]

CHAPTER THREE

The roughness change maps used for this work are generated “wasp_map.exe” roughness map generator that is available at KNMI website[10]. It uses the land-use data base LGN3+[34]. The roughness values used for the different land uses are shown in Appendix C. By using the RD coordinates as an input, a roughness change map that has suitable format for use in WASP is generated. The output map was used with some modifications .Of these modifications, assigning roughness length of 0.03 m around the two KNMI sites to account for the fact that the observed wind data is potential wind speed is one of them. The other modifications are based on recommendations given on [17] . These roughness values depend on the density of package and height variation of the buildings of an urban area Table 2.

Description of area Roughness values [m]

Medium height and density :- Residential – one or two story single houses, 0.3-0.8 gardens, small trees ,mixed houses and small shoe warehouse , light industrial Medium height and Density:- Residential –two and three story large or closely spaced, semidetached and row houses, large trees, less than five story blocks of 0.7-0.1.5 flats with open surrounds ,Mixed house with shape ,light industry ,churches, schools. Tall and high density :- Residential-closely spaced2.0 dense urban surroundings major institutional complexes. Table 2 : Roughness length values depending on density of package and height of buildings[17]

Shelter model

Close to an individual obstacle at distance comparable to the height of the obstacle and at heights likewise comparable to the height of the obstacle the wind profile is perturbed [31]. Hence, for addressing this perturbation the obstacle needs to be treated separately using the shelter model. Shelter of an obstacle depends on the distance between the obstacle and site, the height of the obstacle, the height of the point of interest at the site, the length and porosity of the obstacle[31]. Equation 10 gives the reduction of wind speed due to shelter of infinite long two-dimensional obstacle of zero porosity [31].

CHAPTER THREE

( ) ( ) ( ) Equation 10

Where: ( ) ⁄ ( ) : p is porosity (open/total area) :h is height of obstacle :z is height considered :x is Downstream distance

3.3. Factors affecting the prediction process

As mentioned in[32] the factors that affect the WASP prediction process are grouped into the following categories:  Atmospheric conditions The atmospheric conditions that affect WASP prediction occur due to location of predicted and predictor site at different regional wind climate. The existence of two sites in one climatic condition can be shown using Correlation coefficient. However, even when the sites are found under the same regional wind condition due non- standard atmospheric conditions such as atmospheric stability and stratification; prediction errors can occur[35].  Wind speed records While prediction WASP assumes that the two sites (predictor and predicted) are fully correlated. However, if the averaging time is very small this is not always true unless the sites are very close to each other. The measurement time is another factor seen to have effect on the prediction error.  Weibull fit error and wind direction Prediction error can occur while forcing the observed data to fit into the weibull frequency distribution. The directional differences can occur when the incidence flow is changed due to oblique steeps ridges.  Orography The effect of topography is very significant besides atmospheric conditions. Predictions errors can be caused by site ruggedness, flow separation, and use of topographic features beyond the terrain map considered by WASP etc.

CHAPTER THREE

3.4. Ruggedness Index (RIX)

The ruggedness index (RIX) of a given site is the fractional extent of the surrounding terrain which is steeper than a critical slope which is usually 0.3 [32]. It was proposed to give a measure of flow separation in complex terrains, which are outside the operation envelope of WASP. RIX value of zero means that the terrain is within the working envelop of WASP.

It is calculated for a number of sectors originating from site, by dividing each radius into line segments defined by the crossing of the radius with contour lines [36]. The sum of segments whose slope is greater than 0.3 divided by the sum of all the segments (which equals the radius) gives the RIX value of the radius. The Site ruggedness index is then given as the mean of the sector wise RIX values.

Figure 12: Ruggedness index of area around Rotterdam Noord (the thick red Points inside the circle indicate RIX greater than 0.3) It was mentioned in [32] that ∆RIX that is the difference in RIX value between the predictor and predicted site ( ) is a suitable indicator of the prediction performance. If the predictor site is more rugged than the predicted (turbine) site then the wind speed is under predicted and if the turbine (predicted) site is more rugged than the predictor then the wind speed at the predicted site is over predicted. However if the two site have comparable ruggedness index (∆RIX )there is chance for good prediction[32].

CHAPTER THREE

CHAPTER FOUR

4. SYNTHETIC DIGITAL SURFACE MODEL In this chapter, DSM and its software applications are discussed. It also presents the procedure used for creating a smoother synthetic surface that evolves above clusters of buildings.

4.1. Introduction DSMs are topographic models of the top (reflective) surfaces of buildings, trees, towers and other features which are elevated above the „bare earth‟ while Digital Terrain Models (DTM) or Digital Elevation Models (DEM) are elevation models of „bare earth‟ [37]. DSMs or DEMs can be represented as Altitude Matrices in Raster Mode (a grid of Squares) and as isoclines or Triangular Irregular Networks (TIN) [38] . The difference between the DSM and DTM is that the DSM includes the elevation of trees while the DTM does not Figure 13.

DTM/DEM

Figure 13: Difference between DEM and DSM [39] In wind Engineering, DSM is useful for estimation of urban wind profiles and wind loads on tall buildings. It is also helpful for environmental analysis such as shadowing and solar radiation aside from urban planning [40-42].

Automatic measurement of terrain elevation (such as photogrammetry and airborne laser scanning) and cartographic digitizing of topographic maps are the widely used DTM construction methods [38]. Automatic measurement usually results in bulk data which need to pass through various post processes that use different interpolation methods before generating a DTM [38]. For the construction of DSM as well, automatic measurement followed by spatial interpolation method is a commonly used technique.

CHAPTER FOUR

Spatial interpolation is a procedure which is used for predicting the value of a field variable at non-sampled sites within the area covered by the sample locations [43]. Depending on its applications it is divided into different categories;  Exact/Approximate interpolation depending on whether the surface passes through the reference point or there exists few degrees of error.  Local /Global interpolation depending on whether pre-defined nearby points or all the data points influence the interpolated value.  Stochastic/Deterministic interpolation whether it incorporates geo-statistical3 theory to produce surfaces with specific levels of errors or not. Some of the well-known interpolation methods are Inverse distance Weighting, Kriging, Nearest neighbour, Triangulation with linear interpolation, Minimum curvature, and nearest neighbour binning [41, 44].

Comparison of DSMs created using six interpolation methods; nearest neighbour, inverse distance weighting, triangulation with linear interpolation, minimum curvature, kriging and radial basis functions; was performed [41]. It showed that for constant density and distribution of data points all interpolation methods give sensibly the same result. It also showed that if there are less sample densities kriging and radial basis functions are the most robust methods. While in [45], it was pointed out that if the data locations are dense and uniformly distributed there will be a fair estimate of the values despite the interpolation methods.

Even though, no technical advantage is found attached to specific interpolation method; due to its vast application and acceptance in the construction of DSM Kriging interpolation is adopted for the construction of DSM.

4.1.1. Kriging Interpolation

Kriging is the term given for an interpolation technique that uses information about the stochastic aspects of spatial nature. It is based on the assumption that, values at a short distance are more likely to be similar than at a larger distance. Unknown value at a grid node is estimated as a weighted average of the measured values at reference points, Equation 11. The weights are based not only on the distance between measured points and prediction locations but also on the overall spatial arrangement of the measured points which can be expressed using a variogram [46].

3 Geo-statistics is a branch of statistics focusing on spatial data sets 28

CHAPTER FOUR

̂ ( ) ∑ ( ) Equation 11

th Where: Z (si) is the measured value at the i location

th : λi is an unknown weight for the measured value at the i location

: so is the prediction location : N is the number of measured values Kriging is optimal in a sense that the interpolation weights are chosen to optimize the interpolation function hence provide Best Linear Unbiased Estimate (BLUE)4 for the value of a variable at a given point [43]. It uses Semi-variogram to define the weights that determine the contribution of each data point to the prediction of un-sampled values. A detailed mathematical modelling and description of Kriging method can be found in [47-48]. 4.1.1.1. Steps of kriging interpolation The steps of Kriging interpolation as describe in [46] are explained as follows.

 Calculation of Empirical Semi- Variogram This step consists of the calculation of experimental variogram from the measured data using Equation 12. Variogram is a quantitative descriptive statistic that can be represented graphically in a manner that characterizes the spatial continuity (i.e. roughness) of a data set. It shows a texture difference (continuity of high and low zones) of a data set in which common descriptive statistics and histograms fail to identify [49].

( ) ( ( ) ( )) Equation 12

Where: ( ) is variogram of separation ( ) : Z(x, y) is the value of the variable of interest at location (x, y) : is the statistical expectation operator If there are n observed data points, there will be ( −1)/2 unique pairs of observations. Thus, even a data set of reasonable size generates many pairs. This makes it difficult to plot all variograms quickly, and hence the pairs are grouped into lag bins and averaged variogram result of a specific lag distance is used as a representative variogram for the lag distance.

4 BLUE means that it has the smallest variance among all unbiased linear estimators. 29

CHAPTER FOUR

 Variogram model fitting

After the empirical semi-variogram is calculated and plotted against lag distance, the Variogram Model that fits the scatter plot is chosen. The scale and length parameters are adjusted iteratively for a better fit. Figure 14 shows a fitted spherical variogram for 10000 sample points with a scale of 6000 and length of 480.

Column C Direction: 0.0 Tolerance: 90.0 8000 Fitted variogram Model

7000 Experimental variogram

6000 Scale

5000

r g

o 4000

a V

3000

2000

1000 Length

0 0 50 100 150 200 250 300 350 400 450 500 550 Lag Distance

Figure 14: Variogram Model for Sample Data with scale of 6000 and length of 480

 Prediction using the fitted model

Using the spatial information of the raw data (for computing distances) and semi-variogram model, weights are determined and predictions of unknown values are made.

4.1.2. Grid Filtering

Grid filtering uses corresponding grid node and its neighbours in the input grid to recalculate values of output grid nodes [50]. Neighbourhood grids are rectangular sub array of nodes that surround the corresponding grid Figure 15. Several grid filtering methods suited for different purposes; such as smoothing (low pass filters) and sharpening (high pass filter) exist in surfer [50]. These filtering methods are divided into two groups; a linear convolution which computes weighted averages of the neighbouring input grid nodes and a nonlinear filtering method which doesn‟t use a weighted average [50].

CHAPTER FOUR

Maximum filtering method in which the value of output grid node equals the maximum of the neighbouring values is one of the non-linear order statistics filtering method used in this work. The other filtering method used for in this study is moving average, one of the non-linear convolution methods. It computes the value for the corresponding grid node by averaging the values of neighbouring grid nodes.

2 5 7

9 3 11

1 4 9

Figure 15: A 3 by 3-filter size grid showing the neighbouring (green) and corresponding (red) grid nodes 4.2. DSM Application Software For the construction of DSM SURFER or ArcMap computer programs can be used. SURFER developed by US Golden Company, is a contouring and 3D surface mapping program. It contains up to 12 interpolation methods. It can create contours, 3D surfaces, and wireframes, etc. from grids. It has different gridding and contouring methods with more control on the gridding parameters [51]. It is also possible to export the elevation contour in different formats. ArcMap has two extensions Spatial Analyst and Geo-statistical Analyst. The Geo-statistical analyst extension provides several types of interpolation methods, and works best with Raster based data[46].

4.3. Elevation Data SRTM and CAD Shuttle Radar Topography mission (SRTM) is an elevation database which is available for 80% of the world. The data was collected by a radar system flown on board on the space shuttle Endeavour on February 2000. The objective of this mission was to obtain RADAR data of most of the Earth‟s land surface to produce high resolution topographic maps[39]. This data is available in TIFF format on United States Geological Survey (USGS) website [52] with a resolution of approximately 90 meters by providing the latitude and longitude of the area. CAD maps containing elevation data of buildings of the areas of interest can then be draped on the prepared SRTM data to construct a DSM. However, the SRTM data was not used in this study

CHAPTER FOUR for two main reasons. These are the low resolution of the SRTM data and the lack of CAD map of the cities with information about the elevation of buildings.

Actual Height of the Netherlands (AHN) The Actual Height of the Netherlands (AHN) data is an elevation map which is measured with laser meter, a technique in which a plane or helicopter with a laser beam scans the surface[53]. The measurements of duration of laser reflection and the movement of the aircraft together give a precise measurement of the height. This data includes elevation of buildings, trees, and other features on the scanned surface. It was made available by the map room of TUDelft Library in a .xyz data format.

The coordinate system used in the AHN data is the Empire Triangle Coordinate, which is also known as Rijksdriehoekscoördinaten or RD-coordinate. RD-coordinate is a Cartesian coordinate system, which is based on „false origin‟. It has a datum name Amersfoort because the „false origin‟ is located in Amersfoort. Netherlands is found between the RD-coordinates of 20 km and 300 km false Easting (x coordinate) and 300 km to 600 km false Northing of RD coordinate Figure 16 [54].

The AHN data is divided in to grids and assigned particular numbers for easy organisation Figure 17. Each grid cell covers a 40 by 25 sq. km area which is then further divided into 32 smaller cells of area of 5 by 6.25 sq. km and given additional alphabetic representation [53]. The particular sets of cells used for requesting the data from the TUDelft Map room are listed in Table 3.

City AHN data name

Delft 37en1,37en2

Rotterdam 37bz2, 37dn2, 37dz2, 37ez1, 37ez2, 37gn1, 37gn2, 37gz1, 37gz2, 37fz1, 37hn1, 37hz1, Zoetermeer 30hn1,30hn2, Table 3: AHN data sets used for data Request

CHAPTER FOUR

Figure 16: Location of the Netherlands in RD coordinate [54]

CHAPTER FOUR

20 60 100 140 180 220 260

600

575

550

525

500

475

450

425

400

375

350

325

300

Figure 17: AHN data cell representation on RD coordinate system 4.4. Construction of a synthetic surface above the urban area In this section, the steps followed for creating a synthetic surface that evolves above the urban areas are presented. For showing the results of each step, an AHN data covering 3 by 3 sq. km area of Rotterdam is used. All elevations are in centimetre and the coordinates are RD coordinate system. A flow chart showing the steps is included in Appendix A.

Step 1: Grid Formation The purpose of any gridding method is to create a regularly spaced rectangular array of Z values from irregularly spaced XYZ data points using spatial interpolation[41].Each of the AHN data used in this work have 1048576 data points. A gridding space of 5 m is used to get the similar grid node values as that of the AHN data. For the formation of the grid the default linear variogram is used because  The formation of a variogram model for large number of data points takes much computational time and  The xyz data are regularly spaced and hence formation of variogram becomes insignificant.

CHAPTER FOUR

A residual5 of zero that indicates very high accuracy is found while checking the correctness of the interpolation method. The reason for such a high accuracy is the use of 5 m grid size (spacing), which is the same spacing as that of the regularly spaced .xyz AHN data.

The 3D surface model and shaded relief map of the grid are shown in Figure 18. From the maps, the cluster of buildings in an area can be seen. Nevertheless, in the 3D model it is difficult to differentiate buildings and other features of the urban area.

Step 2: - Grid mosaic The Grid mosaic is used to combine different grids to get a larger grid that is helpful in placing all the meteorological sites in one map. In this step, the grid size (resolution) of the bigger map can also be changed to different values.

5 A residual is the difference in elevation values created by interpolation and the unprocessed AHN data 35

CHAPTER FOUR

Figure 18: DSM (Top) and shaded Relief map (Bottom) of the grid where kriging interpolation is applied

CHAPTER FOUR

Step 3: -Grid Filter (Smoothing)

To remove the detailed irregularities in the 3D model and create a surface that evolves above the cluster of building and other features grid smoothing/filtering is a very important step. The filter type, number of times the filter is applied, and the filter size are adjusted using the filtering dialog box. As there are no standard methods suggested for this type of work, a trial and error method with visual inspection was used for choosing suitable combination of grid filtering methods.

 First, the non-linear maximum filtering method with a five by five filter size was used. The DSM is then constructed using the filtered grid. As seen in Figure 19, the DSM shows a clearer view of buildings. Large ruggedness index values were observed while using this surface as an elevation contour.

Figure 19: DSM of filtered grid (Maximum filter size 5 by 5)

 Hence, for creating a smooth synthetic surface that evolves above clusters/groups of buildings a combination of maximum and moving average filtering methods is applied. First, maximum filtering and then moving average methods are applied to smoothen the surface. A maximum filter method with filter size of 25 by 25 was

CHAPTER FOUR

applied to the grid formed in the step one. Further grid filtering was performed by selecting a moving average filter method with 39 by 39 filter size.

Figure 20: 3D model of the double filtered grid  Because of the moving average filtering method at some locations, buildings might go beyond the synthetic surface Figure 21. To include the elevation of the buildings that go beyond the smoothened surface, grid math command of surfer is used. By subtracting, the double filtered grid from the single filtered grid and blanking out all the negative elevations (i.e. buildings covered by surface) the elevation of the buildings above the synthetic surface is calculated. The grid formed with this data is then added to the double filtered grid to find the final elevation data. Figure 20 shows the DSM created using the final elevation grid.

CHAPTER FOUR

Figure 21: Overlaid 3D surface maps of double filtered (grey) and single filterd grid(red)

Figure 22: 3D surface of the final Grid

CHAPTER FOUR

Step 4: - Contouring

In this step, an elevation contour is created from the final grid Figure 23. To study the sensitivity of the contour level on the wind resource; the elevation contours are created with different contour levels. The results were then exported to DXF files with the contour level to be used as an input to WASP map editor.

432000

431000

430000

429000

428000

427000

426000

80000 81000 82000 83000 84000

Figure 23: Elevation contour of the final grid (synthetic surface)

CHAPTER FOUR

Ruggedness index of the DSM and the Synthetic surface

The following table shows summary of the sector wise RIX calculated for areas around Geulhaven and Rotterdam Noord are shown Table 4. Threshold of 0.3 and radius of 2500 m were used. From the table it can be seen that the individual ruggedness index is lower for the synthetic surface. Having the lower ruggedness index will show that the terrain is relatively gentle and suits the WASP analysis better.

Geulhaven Rotterdam Noord

Synthetic DSM Synthetic DSM surface surface

Total RIX % 0.22 2.66 0 4.27

1 0 0.84 0 5.78 2 0 1.07 0 4.42 3 0 3.33 0 3.98 4 0.33 2.46 0 0.36 5 0.18 0.94 0.1 2.49 6 0.0 2,64 0 3.74 7 0.11 3.32 0 4.92 8 0.26 4.8 0.2 4.14 9 0.03 6.79 0 5.63 10 0.47 3.25 0.19 6.74 11 0.11 5.57 5.54 12 0.68 2,97 0 3,93 13 1 3.59 0 2.72

14 0.38 0.24 0 3.92

15 0 0.39 0 4.67 16 0 0.3 0 5.59 Table 4: Summary of Ruggedness Index for Geulhaven and Zestienhoven

CHAPTER FOUR

CHAPTER FIVE

5. WIND SPEED DATA

Long-term wind data with dense measurement masts are very important for wind resource analysis. In this chapter, the meteorological station and the short-term measurement data used for this work are discussed.

5.1. Historical Wind Data The HYDRA project, which started in May 1998 and ended in November 2005 was first organised to do risk assessment on the Dutch dike systems but later it ended up yielding important information for the Wind Energy Communities. There are more than 50 measuring stations throughout the Netherlands[34]. For some of the stations, the measurement started in the early ‟50s. The wind speed and direction are recorded with a resolution of 0.1 m/s and 10⁰ respectively. The data was very well examined by KNMI and was given different quality codes. The codes show if the data is valid, questionable, added, or corrected manually etc. From the website hourly „potential wind speed‟, direction, date, time and quality code can be downloaded freely in ASCII format [34]. Potential wind speed is wind that is corrected for the effects of shelter from buildings or vegetation i.e. wind speed at 10 m height with the station surrounding flat and roughness length of 0.03 m (which is equal to that of grass) and free of obstacles [34].

The quality of the data that is collected from the measuring stations is of great importance for quality of the wind map. For this study, a potential wind with a “valid data” code is directly used without further quality assessment. Information about the location of the measurement mast, measurement height, the measurement, and recording equipment, and status of the station were documented. They can be accessed from the website [34]. All these information allow relying on the KNMI data possible. The meteorological stations used are discussed in detail in Sections 5.1.1 and 5.1.2.

CHAPTER FIVE

5.1.1. Station 344: Zestienhoven

Zestienhoven is found at latitude of 51.955⁰ and longitude of 4.444⁰ or X=90125 m and Y=44100 m RD coordinate. The measurement period for this station starts from 1981. However for this study the recent years measurement data (January 2001 - December of 2009) are used. The wind speed and direction are analysed using WASP utility package OWC to get site-specific wind climate. A directional binning of 16 sectors each with 22.5⁰, which is similar to the console binning of the measurement mast of the Rotterdam Noord Police station, is used.

Figure 24 shows the result from OWC analysis: the Weibull distribution and wind rose. The mean wind speed and power density are 4.94 m/s and 152 W/m2. It can be observed that the prevailing wind directions are south and southwest direction (sectors 9, 10, 11, 12, and 13) with an overall frequency of 39%. The strongest wind is 6.45 m/s from sector 12.

1 16 2 15 1 3

14 4

13 5

12 6

11 7 10 9 8

Figure 24 : Observed Wind Climate of Zestienhoven

CHAPTER FIVE

5.1.2. Station 343 Rotterdam Geulhaven

This station is located at latitude of 51.893⁰ and longitude of 4.313⁰ in RD coordinate. Even though the measurement for this meteorological station starts from 1981, the data registered from January 2001 until December 2009 is used for this study.

The Weibull distribution and the wind rose from OWC analysis are shown in Figure 25. The mean wind speed, and power density for this site are 5.71 m/s and 196 W/m2 respectively. The prevailing wind directions are south and southwest (sectors 9, 10, 11, 12) with a total frequency of 36.2% sectors 9 and 11 each contributing more than 10 % each. The strongest wind of 7.25 m/s is registered for sector 12

1 16 2 15 3

14 4

13 5

12 6

11 7 10 9 8

Figure 25: Observed Wind Climate of Geulhaven

CHAPTER FIVE

5.1.3. Comparison of Zestienhoven and Geulhaven

Even though the wind speeds are standardized to topographies of flat terrain and roughness length of 0.03 m, there is a difference of 0.77 m/s in mean wind speed with Geulhaven having the higher value. This is because Geulhaven is located near to the coast of North Sea and river ‟Nieuwe Maas‟. For both sites, the strongest wind comes from sector 12.

Zestienhoven Geulhaven Frequency Zestienhoven Geulhaven Sector Difference No. Frequency Wind Speed Wind Speed Frequency [%] (fz –fg ) [%] [m/s] [m/s] 1 6.8 3.34 6.1 4.12 0.7 2 3.1 3.41 4.5 4.12 -1.4 3 5.9 4.08 5.7 4.69 0.2 4 5.4 4.15 4.0 4.69 1.4 5 6.5 4.31 5.6 5.46 0.9 6 3.7 3.92 2.7 5.61 1 7 1.9 3.65 2.9 4.67 -1 8 4.2 4.32 5.5 4.41 -1.3 9 11.3 5.03 12.3 5.81 -1 10 8.5 6.30 4.9 6.65 3.6 11 9.5 6.35 11.6 7.10 -2.1 12 9.7 6.45 7.4 7.25 2.3 13 11.3 4.98 9.5 6.70 1.8 14 4.7 4.81 5.1 6.06 -0.4 15 3.4 4.78 6.1 5.56 -2.7 16 4.0 4.53 6.2 4.88 0.7 All 4.94 5.71

Table 5: Observed wind climate comparison of the two KNMI stations

CHAPTER FIVE

In addition to the difference in the mean wind speed, a sector wise frequency difference exists between the two KNMI stations. The maximum frequency differences are seen in the southwest direction especially sectors 10, 11, and 12. The wind speed frequency of Geulhaven is higher than that of Zestienhoven for sectors 9 and 11 but lower for sector 10 and 12 Figure 26. This is because of the neighbourhood topography that deflects the wind flowing in the direction of sector 10 (from southwest direction; which is the prevailing wind direction in the Netherlands) to sectors 11 and 9 Figure 27.

Figure 26: Sector wise wind speed frequencies of the KNMI stations

Figure 27: Blockage effect of the neighbouring areas for Geulhaven [Google earth]

CHAPTER FIVE

5.2. Short Term Wind Data

Rotterdam Noord Police station The wind data collected from the Rotterdam Noord police station is used for Validation .The building has a height of 35 m and is located at latitude of 51⁰ 54‟0‟‟ N and longitude of 4 ⁰ 30‟0” E or in RD coordinate. The Davis Vantage Pro2 weather stations were installed in October 2009 (stations 1, 2, 3, and 4 at an elevation of 5 m). The middle station (station 5) was installed on a 9 m pole on November 2010. The data from these stations were used to estimate mean wind speed and power density of the different locations of the site. The setup has then been changed from January 2010 onwards to perform wind shear analysis. The measurement heights of the new setup are 9 m, 7 m, and 5 m for TOP, MIDDLE and BOTTOM weather stations respectively Figure 28. A 10-minutes logging interval was selected, and a visit was made to the station every two weeks to collect the data.

Figure 28: Old measurement setup (left) and new measurement setup (right) of Rotterdam Noord Police station For analysing the wind data for the Rotterdam Noord police station, the wind data from „station 5‟ of the old setup and „TOP station‟ of the new setup are combined. The wind data from November 11, 2009 until October 15 of 2010 are used. Some wind data are lost due to malfunctioning of the console and failure to collect the data on time.

CHAPTER FIVE

Figure 29 shows the results from the OWC analysis. The prevailing wind direction is south (sectors 8, 9 and 10) with a total frequency of 26.5 %. However, sectors 1 and 16 also have high frequency. The 10-minute average wind speed for the measurement period is 4.42 m/s and strongest wind speed of 5.57 m/s is registered for sector 10. 1 16 2 15 1 3

14 4

13 5

12 6

11 7

10 9 8

Figure 29 : Observed Wind Climate of Rotterdam Noord Police station The statistics of the wind data used for this work are summarized in Table 6. Of the different types of prediction errors of WASP, one of them occurs while trying to fit the observed wind data into a weibull distribution. It is quantified using percentage error. Table 5 shows the weibull fit error for all the sites.

Weibull Weibull Mean Weibull Station Measurement Period shape factor scale factor wind speed Fit errors K A [m/s] U [m/s] [%] Geulhaven Jan 2001 - Dec 2009 2.21 6.4 5.71 0.36

Zestienhoven Jan 2001 - Dec 2009 1.81 5.5 4.94 0.49 Rotterdam Nov 11 2009 - Oct 15 Noord Police 1.95 5.0 4.42 0.67 2010 station Table 6: Summary of the Observed Wind Climates

CHAPTER FIVE

5.3. Wind Speed Correlation The two meteorological stations, Geulhaven and Zestienhoven, are 11.4 km apart. A zero time lag cross correlation between the meteorological stations was calculated neglecting the directional data to check if the two stations have a strong linear correlation for the whole measurement period; i.e. from January 2001 until December 2009. The results show that there is a strong linear correlation between the hourly mean wind speeds of the two meteorological stations Table 7. This shows that the two stations are found in the same regional wind climate. It was not possible to calculate the cross correlation between the Rotterdam Noord police station and the two meteorological sites due to the difference in the measurement period.

Station 344 Zestienhoven Station 343 Geulhaven

Station 344 Zestienhoven 1.0000 0.9164

Station 343 Geulhaven 0.9164 1.0000

Table 7: Zero time lag Correlation between the KNMI stations

CHAPTER SIX

6. APPLICATION OF WASP ANALYSIS This chapter discussed the results of WASP analysis performed by using the synthetic surface as a topographic map.

6.1. Cross prediction In WASP, the use of more than one meteorological station to define a wind atlas is not possible. However, the different masts/meteorological sites can be used to validate the wind resource modelling by performing a systematic set of comparative predictions known as cross prediction. It is the procedure of using one meteorological station to calculate the regional wind climate and then use it to perform prediction for the wind climate at the second station; and then repeating the procedure but using the second station as a predicting station. In this study, the two meteorological stations, Geulhaven and Zestienhoven are used for cross prediction. In addition, prediction was made for Rotterdam Noord Police station using the two meteorological stations.

A synthetic surface of size 19.995 by 18.745 sq. km was used as topographic map. The synthetic surface was created using 12 AHN topographic data sets shown in Figure 30. It has a resolution of 5 m and contour level of 3 m.

37bz2 37ez1 37ez2 37fz1

18.745 Km 37dn2 37gn1 37gn2 37hn1

37dz2 37gz1 37gz2 37hz1

19.995 Km

Figure 30: AHN data used for creating the DSM used in cross prediction The roughness change map used in this analysis is generated by the KNMI „wasp_map.exe’ and then modified to consider the standardized „potential wind speed‟. An area of 0.04 sq. km around both meteorological sites is assumed to have a roughness length of 0.03 m. A change in these areas creates a difference in the wind speed prediction.

CHAPTER SIX

For evaluating the prediction performance, the results of each analysis are compared to the Observed Wind Climates using the statistical measurements Root mean Square Error (RMSE) and Percentage error (P.E.), which are calculated using Equation 13 and Equation 14 respectively.

∑ ( )

Equation 13

( ) Equation 14

Where: is the observed wind speed and is the predicted wind speed : n is number of sectors The difference in prediction of the KNMI was also checked using percentage Difference (P.D.) that is calculated using Equation 15.

Equation 15 | |

Where: wind speeds predicted using the two different meteorological sites 6.1.1. Zestienhoven as a Predictor Site Wind Atlas Using the measured wind data and description of terrain (contour map of the synthetic surface and roughness change maps) wind atlas or regional wind climate of Zestienhoven is calculated in the form of weibull parameters and wind roses. The wind atlas data for the different roughness classes and elevations are summarised in Table 8. Figure 31 shows the wind atlas for R-class 0 (roughness length 0 m) and 10 m elevation.

1 16 2 15 1 3

14 4

13 5

12 6

11 7

10 9 8 Figure 31: Wind Atlas for R-class 0 at 10 m

CHAPTER SIX

R-class 0 R-class 1 (0.03 R-class 2 R-class 3 R-class 4 (0.00 m) m) (0.10 m) (0.40 m) (1.50 m) Height 1 m/s 8.59 6.14 5.35 4.21 2.8 (z = 10 m) Wm-2 726 293 193 93 27 Height 2 m/s 9.3 7.25 6.53 5.49 4.21 (z = 25 m) Wm-2 917 461 336 200 90 Height 3 m/s 9.82 8.23 7.54 6.54 5.34 (z = 50 m) Wm-2 1071 631 488 322 176 Height 4 m/s 10.32 9.46 8.75 7.76 6.59 (z = 100 m) Wm-2 1276 893 710 498 308 Height 5 m/s 10.76 11.2 10.39 9.3 8.07 (z = 200 m) Wm-2 1540 1477 1173 842 552 Table 8: Summary of wind atlas of Zestienhoven for different elevations and roughness classes

A trend line has been fitted for the wind speeds of each roughness classes at different elevations Figure 32. From the trend lines, it can be seen that the wind speed increases with an increase in elevation and a decrease in roughness class. For R-class 0, there is steeper trend line showing a small increase in wind speed with elevation. This resulted in a lower wind speed for R-class 0 than R-class 1 at an elevation of 200 m. The reason for the steeper trend line for R-class 0 is the low surface roughness of water that causes a small increase of wind speed with elevation when compared to the increase on a land surface. This results in smaller wind speed over a water body than on land at a very high elevation.

Figure 32: Wind atlas trend lines (wind shear) for different roughness classes

CHAPTER SIX

Self-Prediction for Zestienhoven

Figure 33 shows the weibull distribution and wind rose of the self-predicted Wind climate of Zestienhoven. The red and green colours show the frequency deviations of predicted and observed wind climates. These deviations are put in percentage in table. As mentioned in help manual of WASP 10 [33] these differences arise due to the limited resolution (speed and direction) when analyzing the time-series of measured wind.

1 16 2 15 3

14 4

13 5

12 6

11 7 10 9 8

Figure 33: Self-Predicted wind climate of Zestienhoven

CHAPTER SIX

Prediction for Geulhaven

After the wind atlas is created, prediction was made for Geulhaven Meteorological site. The sector wise wind speed and percentage error are calculated and tabulated as shown in Table 9. Maximum sector wise percentage difference of 26.87 % for sector 4 and the minimum of -0.21% for sector 3 are observed. The maximum wind speed for both predicted and observed wind climates is observed for sector 12. The omni directional (all sector) wind prediction has a percentage error of 8.41 %.

Sector Sector OWC PWC Percentage # angle [°] U [m/s] U [m/s] Error [%] 1 0 4.12 3.43 -16.75 2 22.5 4.12 3.84 -6.8 3 45 4.69 4.68 -0.21 4 67.5 4.69 5.95 26.87 5 90 5.46 6.7 22.71 6 112.5 5.61 5.25 -6.42 7 135 4.67 5.55 18.84 8 157.5 4.41 4.94 12.02 9 180 5.81 5.31 -8.61 10 202.5 6.65 8.16 22.71 11 225 7.1 7.74 9.01 12 247.5 7.25 8.84 21.93 13 270 6.7 6.1 -8.96 14 292.5 6.06 6.36 4.95 15 315 5.56 5.71 2.7 16 337.5 4.88 4.94 1.23 All All 5.71 6.19 8.41 Table 9: Difference between predicted and observed wind Climates of Geulhaven

CHAPTER SIX

The wind roses of predicted and observed wind climates are put side by side for easy visual comparison Figure 34. The wind rose of the PWC has the same shape as that of Zestienhoven (predictor), which resulted in large sector wise frequency discrepancies between the observed and predicted site of Geulhaven. As seen in Figure 34, the value of the sector wise frequency of the predictor (Zestienhoven) and predicted site (Geulhaven) are comparable for each sector.

1 1 16 2 16 2 15 3 15 3

14 4 14 4

13 5 13 5

12 6 12 6 11 7 11 7 10 9 8 10 9 8 OWC PWC Figure 34 : Wind Speed Frequency difference

Figure 35: Frequency difference of Observed and Predicted wind climate of Geulhaven and the Predictor site Zestienhoven

CHAPTER SIX

Prediction for Rotterdam Noord Police Station The prediction performed for the Rotterdam Noord police station gave a comparable omni directional annual mean wind speed to that of the observed wind climate with only -0.45% error. However, the sector wise wind speeds have very large prediction errors of up to a -34.72% and 34.67 % for sector 1 and 6 respectively and minimum of -1.61% for sector 16.

Sector Sector OWC PWC Percentage Error # angle [°] U [m/s] U [m/s] [%] 1 0 4.81 3.14 -34.72 2 22.5 4.26 3.83 -10.09 3 45 3.94 4.19 6.35 4 67.5 4.05 4.42 9.14 5 90 3.89 4.2 7.97 6 112.5 3.55 4.78 34.65 7 135 4.39 3.54 -19.36 8 157.5 4.7 3.41 -27.45 9 180 4.96 3.71 -25.2 10 202.5 5.57 4.6 -17.41 11 225 4.16 4.92 18.27 12 247.5 3.94 5.19 31.73 13 270 4.11 4.87 18.49 14 292.5 4.08 4.76 16.67 15 315 3.92 4.84 23.47 16 337.5 4.35 4.28 -1.61 All All 4.42 4.4 -0.45 Table 10: Difference between predicted and observed wind climates of Rotterdam Noord Police Station

CHAPTER SIX

The wind rose of the predicted wind climate has the same shape as that of the observed wind climate of the predictor site. The sector wise frequencies of the predicted site are also comparable to the predictor (Zestienhoven) site Figure 37. Hence, large frequency variation is seen between the observed and predicted wind speeds.

1 1 16 2 16 2 1 15 1 3 15 3

14 4 14 4

13 5 13 5

12 6 12 6

11 7 11 7 10 9 8 10 9 8 OWC PWC Figure 36 : Wind Speed Frequency difference of observed and predicted wind climate of Rotterdam Noord station

Figure 37: Frequency distributions of observed, predicted wind climates of Rotterdam Noord and Zestienhoven (predictor)

CHAPTER SIX

6.1.2. Geulhaven as predictor

Using Geulhaven meteorological site and the same procedures as in Section 6.1.1 predictions were performed for Zestienhoven and Rotterdam Noord Police station. The wind atlas data are summarised in Table 11. Figure 38 shows the wind atlas for a height of 10 m and Roughness class of zero.

1 16 2 15 1 3

14 4

13 5

12 6

11 7

10 9 8 Figure 38: wind Atlas of Station Geulhaven for R-class 0 and elevation 10 m

R-class 0 R-class 1 R-class 2 R-class 3 R-class 4

(0.00 m) (0.03 m) (0.10 m) (0.40 m) (1.50 m) Height 1 m/s 8.31 5.88 5.12 4.03 2.69 (z = 10 m) Wm-2 545 214 141 68 20 Height 2 m/s 8.99 6.99 6.28 5.28 4.06 (z = 25 m) Wm-2 686 342 249 148 67 Height 3 m/s 9.5 8 7.31 6.33 5.18 (z = 50 m) Wm-2 802 482 371 244 133 Height 4 m/s 9.98 9.34 8.6 7.6 6.44 (z = 100 m) Wm-2 959 729 568 392 241 Height 5 m/s 10.4 11.33 10.42 9.27 8.01 (z = 200 m) Wm-2 1158 1306 1015 715 460 Table 11 : Summary of Wind atlas data for Geulhaven

CHAPTER SIX

Self-Prediction for Geulhaven The weibull distribution and wind rose were shown in Figure 39. As described earlier sections the green and red colours show the sector wise frequency difference between predictor and predicted site.

1 16 2 15 1 3

14 4

13 5

12 6

11 7 10 9 8 Figure 39: Self predicted wind speed of Geulhaven

Prediction for Zestienhoven

Wind Climate prediction and comparison with the OWC were done for Zestienhoven meteorological station. Large frequency differences are seen for all the sectors. The predicted wind rose has similar shape and comparable sector wise frequency to that of Geulhaven station (predictor). Figure 40

1 1 16 2 16 2 1 15 1 3 15 3

14 4 14 4

13 5 13 5

12 12 6 6

11 7 11 7 10 8 10 9 8 9 OWC PWC Figure 40: Frequency difference of predicted and observed wind climate of Zestienhoven

CHAPTER SIX

Figure 41: Frequency distributions of observed and predicted wind climate of Zestienhoven and the predictor site The results of the prediction are summarised in Table 12. A maximum percentage difference of 30.54% is observed for sector one and minimum of 0.92% for sector 8. The omni directional (all sector) percentage difference is - 0.81%.

Sector Sector OWC U [m/s] PWC U [m/s] Percentage # angle [°] Error [%] 1 0 3.34 4.36 30.54 2 22.5 3.41 4.04 18.48 3 45 4.08 4.2 2.94 4 67.5 4.15 3.49 -15.9 5 90 4.31 3.7 -14.15 6 112.5 3.92 4.41 12.5 7 135 3.65 3.42 -6.3 8 157.5 4.32 4.28 -0.93 9 180 5.03 5.59 11.13 10 202.5 6.3 5.31 -15.71 11 225 6.35 5.9 -7.09 12 247.5 6.45 5.33 -17.36 13 270 4.98 6.02 20.88 14 292.5 4.81 4.86 1.04 15 315 4.78 4.87 1.88 16 337.5 4.53 4.59 1.32 All All 4.94 4.9 -0.81 Table 12 : Difference between predicted and observed wind climate of Zestienhoven

CHAPTER SIX

Prediction for Rotterdam Noord Police station The sector wise wind speeds predicted using Geulhaven are summarised in Table 13. Maximum percentage difference of 37.22% for sector 13 and a minimum of -0.91% for sector 16 are found. The omni directional wind speed prediction has a percentage error of -3.85%.

Sector Sector OWC PWC Percentage # angle [°] U [m/s] U [m/s] Error[%] 1 0 4.81 3.97 -17.46 2 22.5 4.26 4.34 1.88 3 45 3.94 4.15 5.33 4 67.5 4.05 3.65 -9.88 5 90 3.89 3.52 -9.51 6 112.5 3.55 4.54 27.89 7 135 4.39 3.1 -29.39 8 157.5 4.7 3.24 -31.06 9 180 4.96 4.04 -18.55 10 202.5 5.57 3.84 -31.06 11 225 4.16 4.48 7.69 12 247.5 3.94 4.4 11.68 13 270 4.11 5.64 37.23 14 292.5 4.08 4.57 12.01 15 315 3.92 4.79 22.19 16 337.5 4.35 4.31 -0.92 All All 4.42 4.25 -3.85 Table 13: Difference between predicted and observed wind climate of Rotterdam Noord station

CHAPTER SIX

The Frequency discrepancy for this prediction is shown in Figure 42. As observed in the previous predictions similar shape of the wind rose and comparable sector wise frequency percentages as that of the respective predictor site (Geulhaven) are observed.

1 1 16 2 16 2 15 1 3 15 1 3

14 4 14 4

13 5 13 5

12 6 12 6

11 7 11 7

10 9 8 10 9 8 PWC OWC Figure 42: Frequency difference predicted and OWC (Rotterdam Noord Police station)

Figure 43: Frequency distribution of Observed and predicted wind climate of Rotterdam Noord and the Predictor site Geulhaven

CHAPTER SIX

Summary of the Cross Prediction

By using the default WASP parameters, the following predictions were made for the two meteorological sites. Even though the prediction for Zestienhoven is relatively good, higher prediction errors were observed for Geulhaven.

Zestienhoven Geulhaven Measured Predictor Omni directional Mean Omni directional Mean Omni directional Mean /Predicted site wind speed [m/s] wind speed [m/s] wind speed [m/s]

Zestienhoven 4.94 6.19 4.94

Geulhaven 5.74 4.9 5.71

Table 14: Cross prediction results Table 15 shows the percentage error and RMSE. It can be seen that even though lower percentage error in the omni directional wind speed is seen for Rotterdam Noord when predicted using Zestienhoven higher RMSE indicating larger sector wise prediction errors is observed.

Predictor site Predicted site Percentage Error in Omni RMSE Directional wind speed[%]

Geulhaven -8.41 0.68 Zestienhoven Rotterdam Noord Police 0.45 0.86 station Zestienhoven 0.81 0.4 Geulhaven Rotterdam Noord Police 3.85 0.83 station Table 15: Summary of statistics of cross predictions

CHAPTER SIX

6.2. Comparison of predicted wind speeds of Rotterdam Noord

The Percentage difference of the two Predicted wind speeds of Rotterdam Noord Police station was calculated. Most of the sector wise predictions have larger percentage difference than the omni directional predictions. Combinations of various factors lead to the prediction differences. The first one is the distance between the predictor and Predicted site. Having a reference site near to the prediction site gives a better prediction performance. The other factor is the in between predictor and predicted sites. It was found that the between Geulhaven and Rotterdam Noord is larger than that of Zestienhoven and Rotterdam Noord. Hence, predictions made by Zestienhoven are better.

Sector Sector PWC(Zestienhoven) PWC(Geulhaven) Percentage # angle [°] U [m/s] U [m/s] Difference[%]

1 0 3.14 3.97 23.35 2 22.5 3.83 4.34 12.48 3 45 4.19 4.15 0.96 4 67.5 4.42 3.65 19.08 5 90 4.2 3.52 17.62 6 112.5 4.78 4.54 5.15 7 135 3.54 3.1 13.25 8 157.5 3.41 3.24 5.11 9 180 3.71 4.04 8.52 10 202.5 4.6 3.84 18.01 11 225 4.92 4.48 9.36 12 247.5 5.19 4.4 16.48 13 270 4.87 5.64 14.65 14 292.5 4.76 4.57 4.07 15 315 4.84 4.79 1.04 16 337.5 4.28 4.31 0.7 All All 4.4 4.25 3.47 Table 16: Prediction similarity in using Geulhaven and Zestienhoven as predictor

CHAPTER SIX

Frequency difference of the predictor sites also has a contribution for the prediction differences. Figure 44 shows the relation between the sector wise percentage difference in the predicted wind speeds of Rotterdam Noord and the Predictor sites (Zestienhoven and Geulhaven). For most of the sectors, the highs and lows of the predictor‟s frequency difference match the high and lows of frequency difference of the two predictions. For majority of the sectors higher wind speed prediction differences occur when large predictors‟ frequency differences occur.

Percenatage Difference [%] Difference Percenatage 10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Sector Number Sector Wise Wind Speed Difference(Rot'm Noord) Frequency Difference of predictor sites(Gulhaven and Zestienhoven)

Figure 44: Relation between the prediction differences and Frequency difference of the predicted sites

CHAPTER SIX

6.3. Ruggedness index The total of cross prediction results are plotted against the prediction errors. It is seen that if values are positive wind speed is under predicted but if negative then the wind speed is over predicted. Because there are a few numbers of predictions, a good trend line could not be fit. Hence, it is not reasonable to use a fitted line of this graph to estimate a range of prediction error.

∆Rix[%] -6 -4 -2 0 2 4 6 8 10 -10

-20

-30

-40 Prediction Error in Wind speed[%]

Figure 45: Wind speed prediction error versus

6.4. Effect of Contour level The influence of contour level on prediction accuracy has been investigated using the predicted wind speeds of Rotterdam Noord Police station and cross prediction results. Elevation contour levels of 3 m, 8 m, 10 m, 15 m, and 20 m were used. As seen in Figure 46 with increase in the contour level the prediction error of the omni directional wind speed for Rotterdam Noord and Geulhaven decreases. However, for station Zestienhoven the opposite trend is observed. It was also observed that with decrease in contour level the has increased for all the staions. Even though this should have reduced the predciton error it was not the case for Zestienhoven.The same thing is also seen in sectorwise predictions,for most of the underpredicted wind speeds the increase in contour level increases their prediction error however for the overpredicted wind speeds reduction in prediction errors is observed.

CHAPTER SIX

Figure 46 : Variation of omni directional prediction error with contour level

40 3m 20 8m 0 10 m 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 -20 15m

Percentage Error[%] Percentage -40 20m

-60 Sector Number

Figure 47: Effect of contour level on the Sector wise predictions (Rotterdam Noord) using Geulhaven as predictor

CHAPTER SEVEN

7. RESULTS AND DISCUSSIONS

This section presents an overview of the wind maps developed for Delft, Rotterdam, and Zoetermeer using the methodology described in the previous sections.

Introduction Using the methodology described in the previous sections it was possible to develop a 25 m resolution wind map for the areas of Delft, Rotterdam and Zoetermeer and at an elevation of 10 m above the synthetic surfaces covering the areas. This height is selected because the hub height of most UWTs installed on rooftops is around 10 m .An additional 100 m resolution wind map was developed for Rotterdam .The wind maps show the spatial wind speed difference in urban and rural areas. In all of the wind maps, the RD coordinate system is used. The use of Amersfoort program from the KNMI website is recommended to change coordinate of the point of interest.

Rotterdam For the area of Rotterdam 100 m and 25 m resolution wind maps covering area were developed. Zestienhoven meteorological site is used as a predictor site. Within the urban areas, higher wind speeds are observed at elevated places, which in this case are tall buildings. It was also possible to see very low wind speeds in the highly congested urban areas. The 25 m resolution wind maps are presented in Appendix D.

The wind map developed for areas around Zestienhoven meteorological station is shown in Figure 49. It is developed using the wind atlas of Geulhaven. It covers an area of 2.5 by 2.5 sq. km and has a resolution of 25 m. Within the urban areas, higher wind speeds are observed at elevated places, which in this case are tall buildings .The shaded relief map for which the wind map was developed is also shown in Figure 49. It shows the density of package of buildings. From the wind map it is possible to see higher wind speed in lower density building areas and lower wind speed in the highly dense areas.

CHAPTER SEVEN

Figure 48: Wind map for part of Rotterdam

Figure 49 : Shaded relief map of part of Rotterdam for which the wind map was developed

CHAPTER SEVEN

Delft For developing the Delft wind map, the wind atlas generated using the Zestienhoven meteorological station is used. As observed in the previous sections lower wind speeds were observed in the urban areas. However within the urban area where tall buildings and street canyons are located higher wind speed up to 6.9 m/s were observed. The areas where small numbers of buildings are located (open areas) also have higher wind speed. Figure 50 and Figure 51 shows the wind map and shaded relief map of an area.

446400

446200 7 6.8 446000 6.6 6.4 6.2 445800 6 5.8

] 5.6

m 5.4 [

445600 5.2

g 5 n

i 4.8 h t 445400 4.6

r 4.4 o

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Easting[m] Figure 50: 25 m resolution wind map for Part of Delft

CHAPTER SEVEN

Figure 51: Shaded Relief map of part delft for which the wind map was developed

CHAPTER SEVEN

Zoetermeer The wind resource map for Zoetermeer covers an area of 62.5 sq. km .It has a resolution of 25 m. It is developed for an elevation of 10 m above the synthetic surface. For developing this wind map, Zestienhoven meteorological station was used Appendix D (Figure 65). This wind map is developed to show the general trend in the wind speed. There is big uncertainty in its correctness as Zestienhoven is located far from Zoetermeer.

Figure 52 and Figure 53 show the wind map and shaded relief maps of part of Zoetermeer. From these maps, it can be seen that in the open areas high wind speed up to 6 m/s are predicted. However high wind speeds of up to 7.6 m/s were observed in area where high elevation buildings are located. Areas in between the cluster of buildings also have a relatively high wind speed.

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] 5.8

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452600

92000 92200 92400 92600 92800 93000 93200 93400 93600 93800 Easting [m]

Figure 52:25 m resolution wind map for part of Zoetermeer

CHAPTER SEVEN

Figure 53: shaded relief map for the area on which the map was developed

CHAPTER EIGHT

8. CONCLUSIONS AND RECOMMENDATIONS

8.1. Conclusion The large density of buildings and their height variation in the cities resulted in complex Digital surface model i.e. high ruggedness. The implemented model, smooth synthetic surface, however resulted in a better surface in terms of ruggedness index hence the topographic model is within the WASP working envelop.

Large sector wise prediction errors are observed for both stations during cross predictions however, omni directional predictions were relatively better. The wind rose of the Predicted wind climate has similar shape as that of the predictor site, which resulted in high sector wise wind speed and frequency prediction errors. It was also seen that for the particular case tested here Zestienhoven was a better predictor for Rotterdam Noord than Geulhaven.

The influence of contour level on wind speed prediction showed two opposite trends. In case of under prediction, the error decreases with increase in contour level while in case of over predication, the error increases with increase in contour level. Roughness length has Wind speed has large correlation with prediction error.

The wind resource maps show the trend of spatial wind variation. In urban areas, lower wind speeds in range of 3-5 m/s while in transitional areas (rural to urban) and high elevation places higher wind speed were observed. These places are areas where there is good wind energy potential.

The methodology gave a logical first estimate of the wind potential of sites .The results and drawn conclusions are limited to cases tested here .It should be known that the application of WASP for complex terrain might introduce large uncertainties. Furthermore, calculation of turbulence intensity falls out of scope of the study.

CHAPTER EIGHT

8.2. Recommendation

While developing the synthetic surface even though the overall ruggedness index has decreased, the reduction was not uniform. This might result in larger between predictor and predicted sites, which will then result in higher prediction error. Hence devising an approach that reduced the ruggedness index of the synthetic surface uniformly will improve the results.

For improving the results, and reducing one of the many uncertainties of the methodology, use of raw wind data that is not standardized to a certain type of terrain roughness length is recommended.

The use of onscreen digitised roughness length is advantageous to address different types of urban areas but at the same time, it is very subjective. Hence, it must be done with great care. In order to avoid the subjectivity on roughens length use of morphometric (geometric) method which uses Actual DSM for the calculation of roughness length would be helpful.

In this study first estimate of the wind speed in the urban areas is given. The methodology needs further refinement and validation. Installation of more measurement masts in the urban areas is very important for improving and validating the methodology. While doing so installation of the measurement mast in areas where high wind speeds were seen is very advantageous.

REFERENCES

[1] Anonymous, "Wind Energy Integration in The Urban Environment," WINEUR,Intelligent Energy-Europe programme, European Commission 2005. [2] Jadranka Cace, RenCom, et al., "Urban Wind Turbines :Guidelines for Small Wind Turbines in the Built Environment," WINEUR,Intelligent Energy-Europe programme, European CommissionFebruary 2007. [3] D. C. Anderson, et al., "Rooftop Wind Resource Assessment using a Three-Dimensional Ultrasonic Anemometer," in World Wind Energy Conference, Kingston Ontario, 2008. [4] K. Syngellakis and H. Traylor, "Urban Wind Resource Assessment in UK :An introduction to wind resource assessment in the urban environment," WINEUR,Intelligent Energy-Europe programme, European Commission, Chineham, UK; 27 February 2007. [5] Anonymous. April). Donqi Urban Windmill Available: http://www.donqi.eu/engels/initiatiefnemers_2.html [6] Ib Toren and Erik Lundtang Petersen, European wind Atlas. Roskilde: Risø National Laboratory, 2010. [7] Anonymous, "Wandkaart van Nederland op 100 m hoogte," KEMA Nederland B.V., Arnhem , Netherlands; 2005. [8] T. Simões, Instituto Nacional de Engenharia, Tecnologia e Inovação, I.P., Portugal , et al., "A first methodology for wind energy resource assessment in urbanised areas in Portugal," in European Wind Energy Conference and Exhibition, Marseille France, 2009. [9] (2000, The American Meteorological Society's (AMS) Glossary of Meteorology (2nd ed.). Available: http://amsglossary.allenpress.com/glossary [10] G. Crasto, "Numerical Simulation of The Atmospheric Boundary Layer," PhD, Università degli Studi di Cagliari, 2007. [11] M. L. Ray, et al., "Analysis of wind shear models and trends in different terrain's," in American Wind Energy Association:Wind power 2006, Pittsburgh, PA, USA, 2006. [12] M. L. Ray, et al., "Analysis of wind shear models and trends in different terrain's," in American Wind Energy Association Wind power 2006, Pittsburgh, PA, USA, 2006. [13] T. Burton, et al., Wind Energy Handbook. Baffins Lane, Chichester: John Wiley and Sons LTD, 2001. [14] Anonymous, "Report on Resource Assessment," WINEUR,Intelligent Energy-Europe programme, European Commission Deliverable 5.1, February 2007. [15] C. Beller, "Urban Wind Energy - State of the Art " Risø National Laboratory for Sustainable Energy,Technical University of Denmark, Roskilde, Denmark Risø-R-1668, October 2009. [16] S. M. Mertens, "Wind turbines at buildings:Wind energy in the built environment," Delft University of Technology, Delft,Netherlands, 2005. [17] C. S. B. GRIMMOND and T. R. OKE, "Aerodynamic Properties of Urban Areas Derived from Analysis of Surface Form," Journal of Applied Meteorolgoy, vol. 38, pp. 1262-1292. [18] S. Stankovic, et al., Urban Wind Energy, 1st ed. London: Earthscan, 2009. [19] A. G. Dutton., et al., "The feasibility of building mounted /integrated wind turbines (BUWTs): Achieving their potential for carbon emission reduction," Final Report 2005. [20] D. J. Mckenzie, et al., "Considering the Correlation in Measure-Correlate-Predict techniques," in World Renewable Energy Congress, 2008 [21] P. Stangroom, "CFD Modelling of Wind Flow Over Terrain," Doctor of Philosophy, University of Nottingham, Nottingham,UK, 2004. [22] O. Undheim, "The non-linear microscale flow solver 3DWind Developments and validation," Doctoral thesis for the degree of doktor ingeniør, Department of Energy and

Process Engineering, Norwegian University of Science and Technology, Trondheim,Norway, 2005. [23] Anonymous. October 12). WindSim Documentation. Available: http://windsim.com/product-overview/ [24] G. Watson, et al., "Comparison of wind flow models in complex terrain," in European Wind Energy Conference & Exhibition, London,UK, 2004. [25] Erik Berge, Kjeller Vindteknikk AS, Norway , et al., "Wind in complex terrain: A comparison of WASP and two CFD-models," in European Wind Energy Conference, Athens Greece, 2006 [26] Daniel Cabezon , Foundation CENER-CIEMAT Spain, et al., "Comparing linear and non linear wind flow models," in European Wind Energy Conference, Athens, Greece, 2006, pp. 275-279. [27] Erik Berge, Kjeller Vindteknikk AS, Norway , et al., "Combining WASP with the WRF meso-scale model: Evaluation of wind resource assessment for three Norwegian wind farm areas," in European wind Energy conference and Exhibition, Milan Italy, 2007. [28] Helmut P. Frank, et al., "The NumericalWind Atlas :the KAMM/WASP Method," Risø National Laboratory, Roskilde, Denmark Risø–R–1252, June 2001. [29] W. J. Coirier, et al., "Progress Towards A Coupled Mesoscale and Microscale Modeling Capability," in seventh symposium on the urban environemnt, San Diego, CA ,USA;, 2007. [30] Poul Astrup and soren E.Larson. (1999). WASP engineering Flow Model for Wind over Land and sea. [31] Ib Toren and E. L. Petersen, European Wind Atlas. Roskilde,Denmark: Risø National Laboratory, 1989. [32] Anthony J. Bowen and Niels G. Mortensen "WASP prediction errors due to site orography," Risø National Laboratory, Roskilde Denmark2004. [33] "WASP users guide," 10 ed. Roskilde,Denmark: Risø,, DTU National Laboratory 2010. [34] KNMI - Klimaatdata en Advies Potentiele Wind. Available: http://www.knmi.nl/samenw/hydra/index.html [35] Niels, G. Mortensen and and Anthony, J. Bowen "Exploring the limits of WASP : The Wind Atlas Analysis and Application Program," presented at the European Union Wind Energy Guteborg,Sweden, 1996. [36] N. G. Mortensen, et al., "Improving WASP Predictions in ( too ) complex Terrain," ed. Roskilde, Denmark: Wind Energy Department, Risø National Laboratory. [37] H. S. Sharma, Mathematical Modelling in Geographical Information System and Digital Cartography 1st ed. New Delhi: Ahok Kumar Mittal, 2006. [38] G. Droj, "Imroving the Accuracy of Digital Terrain Models," Professor, Militon Frenţiu, Ed., ed. Cluj-Napoca , ROMANIA: Studia University Babes-Bolyai, 2008, pp. 65-72. [39] anonymous. Intermap. Available: http://www.intermap.com/digital-surface-models [40] Cláudio Carneiro, et al., "Assessing Digital Surface Models by Verifying Shadows:A Sensor Network Approach," presented at the 6th International Symposium on Spatial Data Quality, St. John‟s, Newfoundland, Canada, 5-8 July 2009. [41] G. Gonçalves, "Analysis of interpolation errors in urban digital surface models created from Lidar data," in 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Lisbon, Portugal, 2006, pp. 160-167. [42] Yasuo Okuda, et al., "Utilization of Digital Surface Model in Urban Area for Wind Engineering," Technical Memorandum of Public Works Research Institute ,Japan, pp. 312-321, 2003. [43] J. Zhang and M. F. Goodchild, Uncertainty in Geographical Information: Taylor and Francis, 2002.

[44] S. Erdogan. (2009, A comparison of interpolation methods for producing digital elevation models at the field scale. Earth Surface Processes and Landforms 34(3), 366–376. [45] G. Bohling, "KRIGING," Kansas Geological Survey C&PE 940, October 19 2005. [46] ArcGIS Desktop 10 help [Online]. Available: http://help.arcgis.com/en/arcgisdesktop/10.0 [47] E. H. Isaaks and R. M. Srivastava, An Introduction to Applied Geostatistics New York: Oxford University Press, 1989. [48] N. A. C.Cressie, Statistics for Spatial Data. New York: John Wiley and Sons, 1991. [49] R. Barnes, "Variogram Tutorial," ed: Golden Software, Inc. [50] "Surfer 8 : Online Tutorial." [51] Tom Bresnahan and Kari Dickenson Surfer 8 Self-Paced Training Guide. [52] Jarvis A., H.I. Reuter,, A. Nelson,, E. Guevara. (2008, January 20). Hole-filled seamless SRTM data v4. Available: http://srtm.csi.cgiar.org [53] AHN. Available: http://www.ahn.nl/ [54] wikipedia. Available: http://nl.wikipedia.org/wiki/Rijksdriehoeksco%C3%B6rdinaten

APPENDICES

APPENDICES Appendix A The flow chart shows the procedure used for creating a synthetic surface that evolves above the urban area. AHN data

GRID FORMATION (Step 1) (Kriging interpolation)

GRID A1,A2,A3 …

GRID MOSAIC (Step 2)

GRID B

GRID FILTERING (step 3)

Single Filtered Double filtered grid gird (Max 5 by 5) 1. Max (25 by 25) GRID C 2. MAV (41 by 41) GRID D

GRID C - GRID D

Data E DATA PREPARATION  Filter the (.xyz format) data(remove all –ve Z values)  Crate a grid (kriging)

GRID F GRID D + GRID F

Contour GRID G GRID G (Step 4)

END

APPENDICES

Appendix B

Rotterdam

The synthetic surface that is constructed for Rotterdam is shown in Figure 54. The coordinate system used is RD and elevation is in centimetre.

Figure 54 : Synthetic surface above area of Rotterdam

APPENDICES

Figure 55: Snap shot of Rotterdam [source Google earth]

APPENDICES

Delft The 3D synthetic surface and image map used for developing wind map for the area of Delft are shown in Figure 56 and Figure 55.

Figure 56 : Synthetic surface evolving above area of Delft

Figure 57: Snap shot of Delft [Google Earth]

APPENDICES

Zoetermeer

Figure 58:3D synthetic surface evolving above the areas of Zoetermeer

Figure 59: Snap shot of Zoetermeer [Google earth] 85

APPENDICES

Appendix C

The roughness length used in “wasp_map_exe”

ID Zo (m) Class names 0 0.03 no data 1 0.03 grass 2 0.17 maize 3 0.07 potatoes 4 0.07 beets 5 0.16 cereals 6 0.07 other agricultural crops 7 0.15 foreign land 8 0.1 greenhouses 9 0.39 orchards 10 0.07 bulb cultivation 11 0.75 deciduous forest 12 0.75 coniferous forest 16 0.001 fresh water 17 0.001 salt water 18 1.6 continuous urban area 19 0.5 built-up in rural area 20 1.1 deciduous forest in urban area 21 1.1 coniferous forest in urban area 22 2 built-up area with dense forest 23 0.03 grass in built-up area 24 0.001 bare soil in built-up area 25 0.1 main roads and railways 26 0.5 buildings in rural area 27 0.0003 runways 28 0.1 parking lots 30 0.0002 salt marshes 31 0.0003 beaches and dunes 32 0.02 sparsely vegetated dunes 33 0.06 vegetated dunes 34 0.04 heathlands in dune areas 35 0.0003 shifting sands 36 0.03 heath lands 37 0.04 heath lands with minor grass influence 38 0.06 heath lands with major grass influence 39 0.06 raised bogs 40 0.75 forest in raised bogs 41 0.03 miscellaneous swamp vegetation 42 0.1 reed swamp 43 0.75 forest in swamp areas 44 0.07 swampy pastures in peat areas 45 0.03 herbaceous vegetation 46 0.001 bare soil in natural areas Table 17 : Land-use and roughness classes in LGN3+ used by ‘wasp_map.exe’

APPENDICES

Appendix D

Wind Map of Delft

]

[

0 0 0 0 0 0

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] m [ g n i h t r o

N 87

APPENDICES

Wind maps of Rotterdam Figure 60 shows the 100 m resolution wind map of Rotterdam. It shows a pattern of lower wind speed (3-4 m/s) in the urban areas while rural (very lower density of buildings) areas have higher wind speed of 4 to 5 m/s. In areas where transition from the non-built up area to more built up areas occurs the wind speed ranges from 4-5 m/s. In this wind map, however it is impossible to see the wind speed variation within the urban areas. For the 25 m resolution, wind map the area of Rotterdam was divided into four parts. The wind maps of each section are shown in Figure 61 Figure 62, Figure 63 and Figure 64.

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2 3 4 5 6 7 8 9 Figure 60 : A 100 m resolution Wind map for Rotterdam

APPENDICES

]

[

0 0 0 0 0 0 0

3 2 1 0 9 8 7

4 4 4 4 3 3 3

4 4 4 4 4 4 4

] m [ h t r o

N Figure 61: A 25 m resolution Wind map of Rotterdam 1

APPENDICES

436000

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. 4

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. 6

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. 7

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87000 88000 89000 90000 91000 92000 93000 94000

East [m] Figure 62: A 25 m resolution Wind map for Rotterdam 2

APPENDICES

436000

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5 .

434000 2

5 .

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]

. 5

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6 o

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. 8

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9 0

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76000 77000 78000 79000 80000 81000 82000

East [m]

Figure 63 : A 25 m resolution Wind map for Rotterdam 3

APPENDICES

443000

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. 441000 5

6 ]

5 m

[

h 440000 t

r o

4 N

439000 4

3 438000

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87000 88000 89000 90000 91000 92000 93000 94000 East [m]

Figure 64: A 25 m resolution Wind map for Rotterdam 4

Wind map of Zoetermeer

One general Pattern observed in this wind map is the high wind speed variation in the urban areas. For most of the urban areas, the wind speed ranges from 3.5-4.5 m/s. Nevertheless, at the very elevated places (where tall buildings are located), the wind speed reached up 6.5 m/s.

APPENDICES

]

[

0 0 0 0 0 0

6 5 4 3 2 1

5 5 5 5 5 5

4 4 4 4 4 4

] m [ g n i h t r o

N Figure 65 : A 25 m Resolution wind map of Zoetermeer