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Determination of Wind Energy Potentialin Kirklareli-TurkeySedat Ersoz a , Tahir Cetin Akinci b , H. Selcuk Nogay c & GokhanDogan da Vocational School, Cankiri Karatekin University, Cankiri, Turkeyb Department of Electrical & Electronics Engineering, Faculty ofEngineering, Kirklareli University, Kirklareli, Turkeyc Luleburgaz Vocational School, Kirklareli University, Kirklareli,Turkeyd Vocational School of Technical Studies, Kirklareli University,Kirklareli, TurkeyAccepted author version posted online: 09 Mar 2012.Publishedonline: 21 Dec 2012.

To cite this article: Sedat Ersoz , Tahir Cetin Akinci , H. Selcuk Nogay & Gokhan Dogan (2013):Determination of Wind Energy Potential in Kirklareli-Turkey, International Journal of Green Energy,10:1, 103-116

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International Journal of Green Energy, 10: 103–116, 2013Copyright © Taylor & Francis Group, LLCISSN: 1543-5075 print / 1543-5083 onlineDOI: 10.1080/15435075.2011.641702

DETERMINATION OF WIND ENERGY POTENTIAL INKIRKLARELI-TURKEY

Sedat Ersoz1, Tahir Cetin Akinci2, H. Selcuk Nogay3, andGokhan Dogan4

1Vocational School, Cankiri Karatekin University, Cankiri, Turkey2Department of Electrical & Electronics Engineering, Faculty of Engineering,Kirklareli University, Kirklareli, Turkey3Luleburgaz Vocational School, Kirklareli University, Kirklareli, Turkey4Vocational School of Technical Studies, Kirklareli University, Kirklareli, Turkey

Wind energy has become an important source that has begun to be used for energy in allover the world in recent years. In this study, by examining the wind potential of the west-ern region of Turkey in detail, an analysis was made. The data were provided by the StateMeteorology Affairs Kirklareli Office. In Kirklareli region, the energy produced on a 7.8 MWwind plant pattern constituted a model through ANFIS (Adaptive-Network Based FuzzyInference Systems). Then, with Wind Atlas Analysis and Application Program (WASP) andby counting the rates of capacity usage, wind energy potential was examined. In the light ofthe data obtained in the study, it has been determined that the method is quite successful andKirklareli is suitable for wind plant investment.

Keywords: Wind potential; Wind energy; Wasp; ANFIS

INTRODUCTION

In today’s world, energy is the most significant value. That the energy sources areexhaustible, dependence on foreign sources, and environmental factors have made energythe most important value. Today, to produce natural, adequate, economical, and cleanenergy is among the most basic problems of economic and social life. Parellel to thesedevelopments, in Turkey whose industry, economy and population are growing, the energyneed is continually increasing. For this reason, the use of the produced energy very effi-ciently, the increase of the potential which belongs to the alternative and sustainable energysources as well as existing energy sources are of high importance (Evans, Strezov, andEvans 2009; Sahin and Bilgili 2009; Haralambos et al. 2011).

Turkey’s renewable energy policies, the potential determining works of renewableenergy sources in the country overall and its various regions become a topic for many stud-ies in both national and international literatures. Particularly, in recent years, this studyhas drawn the interest of scientists. These studies are on; environment, energy policies

Address correspondence to Tahir Cetin Akinci, PH.D., Department of Electrical & ElectronicsEngineering, Faculty of Engineering, Kirklareli University, Kirklareli, 39100 Turkey. E-mail: cetinakinci@hotmail.com

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and their sustainability in terms of social perspectives (Demirbas 2001) the situation ofthe present fosil fuel sources (Kaygusuz 2002), and the suggestions about more efficientuse of these sources (cogeneration systems) (Akdeniz, Caglar, and Gullu 2002). Besides,there are different researches about both the risks of Turkey’s being dependent on foreignsources for fosil fuel energy sources, and the importance of benefiting from renewableenergy sources (Kaygusuz and Kaygusuz 2002). The researches aiming at determiningwind energy potential in various regions of Turkey also make up another aspect of thesestudies (Celik 2007; Ucar and Balo 2008; Ucar and Balo 2009). In diverse regions of thecountry such as Elazig-Maden (Akpinar and Akpinar 2004), Ugurlu and Aydincik Parts ofGokceada (Eskin, Artar, and Tolun 2008), and Alacati (Mutlu, Akpinar, and Balikci 2009)which have important wind potential, potential analyses have been carried out with dif-ferent methods. In addition, setting up an artificial neural network (ANN) models wheredifferent learning algorithms are used, the wind energy potentials and maps of 12 cen-ters in a study (Sozen, Arcaklioglu, and Ozalp 2004), and 17 centers in a similar study(Sozen et al. 2004) were obtained. In the conducted studies, that the Weibull dispersion ismore appropriate to the dispersion of the data taken from real measurements has also beencompared with comparisons (Akpinar and Akpinar 2004; Sozen, Arcaklioglu, Ozalp 2004;Sozen, Arcaklioglu, Ozalp et al. 2004; Eskin, Artar, and Tolun 2008; Mutlu, Akpinar, andBalikci 2009; Akdag and Guler 2010). In the general-purpose measurements and calcu-lations implemented by Turkish Electricity Affairs Administration, wind energy of manyregions of Turkey was examined. The geographical grading of wind energy in Turkey wasmade as Marmara, northwest Black Sea and Aegean coasts (Suzek 2007).

In this study, the wind energy potential of Kirklareli city which was not examinedin detail beforehand has been analyzed making use of the statistical data of the last threeyears. Besides this, counting the rates of energy and capacity usages produced in the sampleof a 7.8 MW wind plant to be built in this region, the suitability of the region for wind plantinvestment has been researched.

ARCHITECTURE OF THE METHOD

Within this study, Wind Atlas Analysis and Application Program (WASP), one of themost preferred packet programs by commercial firms in wind potential analysis, has beenused. WASP is a PC program for predicting wind climates, wind resources and powerproductions from wind turbines and wind farms (Ozgener, Ozgener, and Dincer 2009;Rehman, Ahmad, and Al-Hadhrami 2010; WASP 2011).

Data Analysis and Application of the ANFIS

While deciding to build a wind plant in any region, firstly at least six month analysisfor that region, and for feasiblity, however, one year analysis is required. This condi-tion is also stated in the Wind Plants Agreement of Turkish Energy and Natural SourcesMinistry (Turkish Electrycity Transmission Corporation 2008 Annual Report; TEIAS2011). According to this agreement, the data in question have to include information offorce and direction which were measured in 1 h or 10 min periods. In addition to this,it will be useful to take moisture and temperature data to control the lifetime of the tur-bines in the field of project. In Turkey, at the phase of presenting Feasibility to the EnergyMinistry, controls are personally made again both by the Ministry and the Electicity AffairsSurvey and Development Administration authorities (Turkish Electrycity Transmission

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DETERMINATION OF WIND ENERGY POTENTIAL 105

Corporation 2008 Annual Report; TEIAS 2011). The places where the measures are taken,and post is erected must represent the region properly. This condition only has a meaningin terms of technique. Whether the selected field is suitable for the construction of a windenergy plant must also be considered separately in terms of physical, environmental andlegal sides.

In this study, the average speed and direction data that the measurement stationfounded by General Directorate of State Meteorology Affairs in Kirklareli at 10 m heighttook at 1 h periods for three years (2007, 2008 and 2009) were used. However, with the aimof determining to what extent these data are reliable, an ANFIS (Adaptive-Network BasedFuzzy Inference Systems) model of test-purpose which uses the data of average moisture,temperature and pressure measured in the same region’s same period of time as an input,and the wind speed as an output was set up. Adaptive-Network Based Fuzzy InferenceSystems-ANFIS is a hybrid method of artificial intelligence that uses the inference featuteof the fuzzy logic with its capacity to be able to calculate and learn (Jang 1993; Jang andSun 1995; Tas 2009). Adaptive (matching) nets are composed of directly knitted knots. Thebasic learning rule in adaptive nets is the steepest descent method (Onat and Ersoz 2011).ANFIS can designate all rules or enables the rules to be designated by the specialist withthe help of data according to the construction made for the considered problem. The basicflow diagram of the ANFIS model was shown in Figure 1.

In this study, five-layer Sugeno type ANFIS model which was made up in MATLAB-Simulink software was used. The calculating algorithm of each layer is detailed in (Jang1993; Jang and Sun 1995; Tas 2009) sources, and learning algorithms are detailed in(Lettau 1969; Jang and Sun 1995; Onat and Ersoz 2011) sources. In Figure 2, the blockdiagram of this model is shown.

In the ANFIS model used in this study which is composed of three inputs and oneoutput (Figure 3), the membership functions are defined and made fuzzy. For the inputvariable set, the membership function was taken as Gaussian functions depending on thecharacteristic of data. The fuzzy inputs are taken as the inputs of the neural network andafter they are being processed with the transfer functions of the net in different layers, fuzzyoutputs are gained. These outputs have lineer membership functions, and being clarified, asingle output can be obtained. In the model in the study, 36 month (1096 data) data wereused and 18 month of these data (548 data) were used for the training of neural network,and the other 18 month (548 data) for test and verification.

The learning parameter of Kirklareli region’s data is R = 0.90494 by ANFIS.Figure 4 and Figure 5 respectively show the results of training and test processes gainedthrough the analysis of raw data. In Figure 6, however, the comparison of the wind speedvariables obtained from the output of ANFIS model with the curves acquired from mete-orological measurements is seen. The obtained results show that the output data of theANFIS model used are reliable enough to be used in the analysis of wind potential, and thedata taken from meteorology are reliable.

THE ANALYSIS OF WIND ENERGY POTENTIAL

At the heart of reaching success of a wind plant in terms of economic and techni-cal aspects lie energy production quantities counted with reliable data. To evaluate windenergy potential in an area at maximum level, wind energy plant projects which were notconstrained in a short time, and whose detailed feasibility works were made are neces-sary. This will help both the investor see ahead of him and the benefiting from the country

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Start

Fuzzificate the data using GENFIS1 or GENFIS2

comands

Determine the iteration number (EPOCH)

Determine the tolerance

Start the training process using ANFIS commands

Stop the training process

Is the tolerancevalue has been reached

?

Verify the results with raw data

End

Yes

No

Figure 1 Flowchart of ANFIS.

sources at maximum level (Lettau 1969; Jang and Sun 1995; Flamos et al. 2011; Nafeh2011; Onat and Ersoz 2011).

At this stage, the meteorological data whose reliability was tested in the formerpart, using WASP packet program, the wind energy potential of the selected regionswas analyzed. The program basically consists of two main layers which are analysis and

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DETERMINATION OF WIND ENERGY POTENTIAL 107

Figure 2 Block diagram of Sugeno type ANFIS model.

Figure 3 Architecture of ANFIS.

practice. Analyzing the raw meteorological data measured for analysis process with timeseries, a substructure was built for wind atlas drawing. At the next stage, eliminating theeffects of factors like particular field conditions (obstacle, roughness, etc.), wind atlas ismade up. These values standardized for wind atlas are used in the climate determina-tion and counting of the wind energy potential of the region in the practice part. In theFigure 7, the basic functions of WASP software and fundamental components it uses aredemonstrated.

The obstacles on the land have very important effects on the wind flow. Particularly,such obstacles as building, tree, rock, and etc. considerably affect the direction and speedof wind. Obstacle also affects the permeability of wind. The increase in the permeabilityand decrease in the obstacle length weaken the screening impact.

The roughness of a land is generally designated with roughness length parameter.The relation between the roughness elements and roughness length was presented by Lettau(1969). WASP software identifies four types of roughness class. According to these rough-ness types, the degree of the region whose potential analysis was realized is determined andadded into the countings taking a suitable roughness value. The researcher who wants tocalculate the wind power potential in any region, should consider these criteria (Bagiorgas,Mihalakakou, and Matthopoulos 2008; Sorensen 2008; Togrul and Kizi 2008; Al-Abbadiand Shafiqur 2009; Qu and Shi 2010;] Yu, Han, and Zhao 2010; Shi, Qu, and Zeng 2011).

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–5–5

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Targets T

Training Outputs vs. Targets, R = 0.90494

Best Linear Fit

Y = T

Training Data Points

Outs

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ear

Fit: Y

= (0.8

2)T

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.8)

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Figure 4 Training of outputs (color figure available online).

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Figure 5 Test regressions of outputs (color figure available online).

Nevertheless, the best method is to go to the region and determine the roughness class. Thepoint to be considered here is that the region must be finely examined and the roughnesstype be determined. Sometimes there may not be regions that properly fit these classes.In this condition, the person making the examination should give a value for roughnessbetween these values relying on his/her experiences.

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DETERMINATION OF WIND ENERGY POTENTIAL 109

0 100 200 300 400 500 600–10

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30ANFIS-Trainning

Sample

Roughness

ANFIS

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0 100 200 300 400 500 600–50

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Figure 6 Training and test graphics of outputs (color figure available online).

Figure 7 Architechture of WASP.

At the first stage, the wind map of the region, average speed and power disper-sion maps, wind rose, Weibull speed, and power dispersions were obtained. Point analysisresults at 10 m which is the height of wind measurement station of the region were givenin Table 1.

On calculating the average values for 10 m height in Table 1, the average wind speedis attained as 5.12 m/s, and power density as 133 W/m2. The wind rose gained as a resultof these measurements was given in Figure 8, and the wind speed histogram of the regionin Figure 9.

The heights of meteorological measurement stations are generally selected as 10 m.Yet, the tower heights of today’s wind turbines range between 50–100 m. For this rea-son, when the point analysis values are retracted to 50 m, the wind speed average becomes7.66 m/s and the power density average becomes 443 W/m2. The principal reason why thevalues are retracted to 50 m is immediate surroundings impediments. The impact of imme-diate surroundings obstacles on average wind speed and energy density declines depending

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Table 1 Point Analysis Results for 10 m Height

Sector [◦] Frequencecy [%] c [m/s] k U [m/s] Power density [W/m2] Roughness

0 0.4 2.7 2.09 2.36 15 −0.51%30 0.6 3.7 1.6 3.31 55 −0.40%60 27 7 3.38 6.26 199 1.32%90 12.4 4 1.75 3.56 61 −1.77%120 1.6 1.4 1.81 1.23 2 4.41%150 0.8 1.6 1.44 1.45 5 0.94%180 0.6 2.2 1.51 2 13 0.76%210 16.7 6.6 3.42 5.95 169 1.93%240 29.9 5.7 2.65 5.07 121 −1.24%270 8.7 4.7 2.33 4.19 75 0.88%300 0.8 3.2 3.04 2.89 21 3.45%330 0.5 3.3 2.87 2.93 22 2.58%

Figure 8 Wind roses of selected religion (color figure available online).

Figure 9 Wind speed histograms of selected religions (color figure available online).

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DETERMINATION OF WIND ENERGY POTENTIAL 111

Figure 10 Wind speed and power density maps of selected region for 50 m hub height (color figure availableonline).

on height beginning from ground level. This impact is observed to decrease under 10% at25 m height, and disappears at 100 m. In this case, the effects of immediate surround-ings obstacles which are close to characteristic values of present day wind turbines can besaid to be less than 1%. The region’s maps of speed and power density obtained for 50 mheight are shown in Figure 10. Another result gained in these analyses is that the effectof these obstacles on average energy density is three times higher compared to the aver-age wind speed. The extent of the effect increases in direct proportion to housing aroundmeteorology stations.

At the next stage, energy quantities that will be produced if a plant consisting of1.3 MW six turbines are built in the region have been analyzed. Besides, for two differenttypes of wind turbines of equal strength (Nordex N60, SWT 62), a comparison has beenmade. The place selected for the construction of the plant on the topographical map of theregion is shown in Figure 11. The wind speed, power production, and propulsion coefficientvalues of the chosen two different turbine models are given in Figure 12.

The tower height of the selected two turbines is 70 m. Therefore, meteorologicaldata were brought to this height using WASP, and point analysis was made. In Table 2, thepositions of the points selected for the turbines and the average wind speed at tower height,and energy potentials are shown.

In Table 3, the annual energy production quantities of the chosen turbines are given.As seen in the power output curve, SWT 62 type wind turbine which can generate higherpowers at lower speeds is a more advantageous choice in terms of energy production. In thisstudy, a comparison was made only in terms of energy production. However, consideringonly this value in turbine selection does not provide enough information for lifetime andunit energy costs.

Technically, the capacity use rate (CUR) of a turbine is important. This value is deter-mined with the proportion of the maximum energy quantity that the turbine can produce toenergy quantity that the region can produce in meteorological conditions. From the Table 3,the total energy amount to be produced is found as 33.168 GWh for Nordex N60 turbine,

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Figure 11 Topographic map of selected region and location of wind turbines (color figure available online).

Figure 12 Power output and thrust coefficient characteristics of selected wind turbines (color figure availableonline).

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DETERMINATION OF WIND ENERGY POTENTIAL 113

Table 2 Point Analysis of Kirklareli Wind Farm at 70 m Tower Height

Site Location [m] H [m] c [m/s] k U [m/s] E [W/m2]

1 (567,798.2, 4,618,947.0) 70 10.7 2.94 9.53 7542 (566,664.2, 4,618,075.0) 70 10.3 2.96 9.18 6723 (569,019.4, 4,620,081.0) 70 9.5 2.79 8.45 5414 (569,804.4, 4,618,947.0) 70 10.3 2.75 9.19 7035 (568,670.5, 4,617,726.0) 70 11.7 2.81 10.43 10126 (567,449.3, 4,616,243.0) 70 10.7 2.81 9.57 783

Table 3 The Comparison of Annual Energy Production Quantities

Site Altitude [m] Hub height [m] Turbine type(1.3 MW)

Annual energyproduction [GWh]

Wake loss [%]

1 379 70 SWT 62 6.171 0.68NORDEX N60 5.711 0.76

2 352 70 SWT 62 5.815 0.62NORDEX N60 5.352 0.68

3 350 70 SWT 62 4.983 0.52NORDEX N60 4.545 0.55

4 367 70 SWT 62 5.774 0.44NORDEX N60 5.332 0.47

5 367 70 SWT 62 6.921 0.36NORDEX N60 6.490 0.4

6 341 70 SWT 62 6.284 0.09NORDEX N60 5.738 0.1

and 35.848 GWh for SWT 62 type turbine. By the division of this value to the number ofturbine, the energy amount to be acquired from a turbine is respectively found as:

AEPSWT62 = 35.848

6= 5.975 GWh (1)

AEPNORDEX-N60 = 33.168

6= 5.528 GWh (2)

The amount of maximum energy that can be produced in a year on the condition thateach turbine works in full capacity is,

AEPmax = 1, 3.8760 = 11, 388 MWh = 11.388 GWh (3)

In this case, the rates of capacity usage are given with the calculations in thefollowing equations;

CURSWT62 = 5.975

11.388= 0.52 (4)

CURNORDEX-N60 = 5.528

11.388= 0.48 (5)

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CUR value for both of the turbines is above 20% which is the world average (TurkishElectrycity Transmission Corporation 2008 Annual Report; TEIAS, 2011). But, here it isassumed that all of the energy produced in the wind plant can be consumed. As this is notalways possible in real practice, the CURs will be lower than the values calculated above.

CONCLUSION

In this study, firstly, using ANFIS model, it was put forward that Kirklareli’s windclimate characteristic has a reliable perpetuity.

At the second stage, WASP packet program was used so that wind energy potentialcould determine the reliability. For a year, in the direction of hourly wind speed and direc-tion information, wind roses, Weibull dispersion tables, average wind speed and powerdensity maps were drawn.

According to the classification made by European Wind Energy Union, the averagewind speeds whose wind energy is high enough to be benefited are respectively accepted as6.5 m/s which is near good, 7.5 m/s good, and 8.5 m/s very good (Onat and Ersoz 2011).According to this criterion, Kirklareli takes place in the good class regions with 7.66 m/sin 50 m and in the very good class ones with 9 m/s in 70 m.

In another classification in which wind energy densities are taken into consideration,the regions under 100 W/m2 are included in weak, the ones between 100–300 W/m2 innear good, the ones between 300−700 W

/m2in good, and the ones above 700 W

/m2in

very good classes (Garrad, 1991; Onat and Ersoz 2011). In this respect, Kirklareli regionhas the good and best energy potentials respectively with 443 and 744 W/m2 for 50 mand 70 m heights. These values are above the world average in terms of capacity factor,and they indicate that the wind potential in the region is suitable for an electric energytransformation plant which is parallelly connected to the interconnected mains.

ACKNOWLEDGMENTS

This project was supported by Kirklareli University Scientific Research Projects CoordinationUnit (KUBAP), (project number: KUBAP/001).

Authors present his special thanks to Naci GURBUZ, who is director of the Kirklareli StateMeteorological Branch, for their valuable contributions in providing of the data used in this research.

REFERENCES

Al-Abbadi, N. M., and S. Rehman. 2009. Wind speed and wind power characteristics for gassim,Saudi Arabia. International Journal of Green Energy 6, no. 2:201–17.

Akdag, S. A., and O. Guler. 2010. Wind characteristics analyses and determination of appropriatewind turbine for amasra-black sea region, Turkey. International Journal of Green Energy 7,no. 4:422–33.

Akdeniz, F., A. Caglar, and D. Gullu. 2002. Recent energy investigestions on fossil and alternativenonfossil resources in Turkey. Energy Conversion and Management 43, no. 4:575–89.

Akpinar, E. K., and S. Akpinar. 2004. Determination of the wind energy potential for maden-elazig,Turkey. Energy Conversion and Management 45, no. 18–19:2901–14.

Bagiorgas, H. S., G. Mihalakakou, and D. Matthopoulos. 2008. A statistical analysis of wind speeddistributions in the area of western greece. International Journal of Green Energy 5, no.1–2:120–37.

Dow

nloa

ded

by [

Geo

rget

own

Uni

vers

ity]

at 0

5:20

06

May

201

3

DETERMINATION OF WIND ENERGY POTENTIAL 115

Celik, A. N. 2007. A techno-economic analysis of wind energy in southern turkey. InternationalJournal of Green Energy 4, no. 3:233–47.

Demirbas, A. 2001. Energy balance, energy sources, energy policy, future developments and energyinvestments in Turkey. Energy Conversion and Management 42, no. 10:1239–58.

Elektrik Isleri Etut Idaresi Genel Mudurlugu (General Directorate of Electrical Power ResourcesSurvey and Development Administration). July 2011. http://www.eie.gov.tr

Eskin, N., H. Artar, and S. Tolun. 2008. Wind energy potential of gokceada ısland in Turkey.Renewable and Sustainable Energy Reviews 12, no. 3:839–51.

Evans, A., V. Strezov, and T. J. Evans. 2009. Assessment of sustainability ındicators for renewableenergy technologies. Renewable and Sustainable Energy Reviews 13, no. 5:1082–88.

Flamos, A., P. S. Georgallis, H. Doukas, and J. Psarras. 2011. Using biomass to achieveeuropean union energy targets-a review of biomass status, potential, and supporting policies.International Journal of Green Energy 8, no. 4:411–28.

Haralambos, S. B., G. Mihalakakou, S. Rehman, and L. M. Al-Hadhrami. 2011. Weibull parame-ters estimation using four different methods and most energy-carrying wind speed analysis.International Journal of Green Energy 8, no. 5:529–54.

Jang, R., and J. Shing. 1993. ANFIS: Adaptive-network-based fuzzy inference systems. Man, andCybernetics 23, no. 3:665–85.

Jang, R., J. Shing, and C. T. Sun. 1995. Neuro-fuzzy modeling and control. Proceedings of the IEEE83, no. 3:378–406.

Kaygusuz, K. 2002. Renewable and sustainable energy use in turkey: A review. Renewable andSustainable Energy Reviews 6, no. 4: 339–66.

Kaygusuz, K., and A. Kaygusuz. 2002. Renewable energy and sustainable development in turkey.Renewable Energy 25, no. 3:431–53.

Lettau, H. 1969. Note on aerodynamic rougness-parameter estimation on the basis of roughness-element distribution. Journal of Applied Meteorology 8, no. 5:828–32.

Mutlu, O. S., E. Akpinar, and A. Balikci. 2009. Power quality analysis of wind farm connected toalacati substation in Turkey. Renewable Energy 34, no. 5:1312–8.

Nafeh, A. E.-S. 2011. Optimal economical sizing of a PV-wind hybrid energy system using geneticalgorithm. International Journal of Green Energy 8, no. 1:25–43.

Onat, N., and S. Ersoz. 2011. Analysis of wind climate and wind energy potential of regions inTurkey. Energy-Elsevier no. 36:148–56.

Ozgener, O., L. Ozgener, and I. Dincer. 2009. Analysis of some exergoeconomic parameters of smallwind turbine system. International Journal of Green Energy 6, no. 1:42–56.

Qu, X., and J. Shi. 2010. Bivariate modeling of wind speed and air density distribution for long-termwind energy estimation. International Journal of Green Energy 7, no. 1:21–37.

Rehman, S., A. Ahmad, and L. M. Al-Hadhrami. 2010. Detailed analysis of a 550-MW installedcapacity wind farm in Saudi Arabia. International Journal of Green Energy 7, no. 4:410–21.

Sahin, B., and M. Bilgili. 2009. Wind characteristics and energy potential in belen-hatay, Turkey.International Journal of Green Energy 6, no. 2:157–72.

Shi, J., X. Qu, and S. Zeng. 2011. Short-term wind power generation forecasting: Direct versusındirect arima-based approaches. International Journal of Green Energy 8, no. 1:100–12.

Sørensen, B. 2008. A new method for estimating off-shore wind potentials. International Journal ofGreen Energy 5, no. 3: 139–47.

Sozen, A., E. Arcaklioglu, and M. Ozalp. 2004. Estimation of solar potential in turkey by arti-ficial neural networks using meteorological and geographical data. Energy Conversion andManagement 45, no. 18–19:3033–52.

Sozen, A., E. Arcaklioglu, M. Ozalp, and E. G. Kanit. 2004. Use of artificial neural networks formapping of solar potential in turkey. Applied Energy 77, no. 3:273–86.

Suzek, F. 2007. Determination of wind potential of Turkey. Master thesis., Istanbul TechnicalUniversity. Institute of Science and Technology. Istanbul, Turkey, 45–50.

Dow

nloa

ded

by [

Geo

rget

own

Uni

vers

ity]

at 0

5:20

06

May

201

3

116 ERSOZ ET AL.

Tas, U. 2009. Design of an educational simulator for modelling and control of physiological systemsusing of artificial intelligence techniques. PhD diss., Marmara University Institute of GraduateStudies in Pure and Applied Sciences, Turkey. 10–13.

Togrul, I. T., and M. I. Kizi. 2008. Determination of wind energy potential and wind speed data inbishkek, Kyrgyzstan. International Journal of Green Energy 5, no. 3: 157–73.

Turkiye Elektrik Iletim Anonim Sirketi. 2008. Faaliyet Raporu (Turkish ElectrycityTransmission Corporation 2008 Annual Report). http://www.teias.gov.tr/Faaliyet2008/2008%20faaliyet%20raporu%20turkce.pdf accessed July 2011.

Ucar, A., and F. Balo. 2008. A seasonal analysis of wind turbine characteristics and wind powerpotential in manisa, Turkey. International Journal of Green Energy 5, no. 6:466–79.

Ucar, A., and F. Balo. 2009. Investigation of wind energy potential in kartalkaya-bolu, Turkey.International Journal of Green Energy 6, no. 4:401–12.

WAsP. 2011. The Wind Atlas Analysis and Application Program. http://www.wasp.dk/Yu, D., X. Han, and J. Zhao. 2010. A method to assess the regional wind power fluctuation based on

the meteorological observing system. International Journal of Green Energy 7, no. 5:516–29.

Dow

nloa

ded

by [

Geo

rget

own

Uni

vers

ity]

at 0

5:20

06

May

201

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