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 EPE  Vol.12 No.1 , January 2020
Assessment of Wind Power Density Based on Weibull Distribution in Region of Junin, Peru
Abstract: This paper appraises the accuracy of methods for calculating wind power density (WPD), by comparing measurement values to the shape and scale parameters of the Weibull distribution (WD). For the estimation of WD parameters, the Graphical method (GP), Empirical method of Justus (EMJ), Empirical method of Lysen (EML), Energy pattern factor method (EPF), and Maximum likelihood method (ML) are used. The accuracy of each method was evaluated via multiple metrics: Mean absolute bias error (MABE), Mean absolute percentage error (MAPE), Root mean square error (RMSE), Relative root mean square error (RRMSE), Correlation coefficient (R), and Index of agreement (IA). The studys objective is to select the most suitable methods to evaluate the WD parameters (k and c) for calculating WDP in four meteorological stations located in Junin-Peru: Comas, Huasahuasi, Junin, and Yantac. According to the statistical index results, the ML, EMJ, and EML methods are the most accurate for each station, however, it is important to note that the methods do not perform equally well in all stations, presumably the graphical conditions and external factors play a major role.
Cite this paper: Galarza, J. , Condezo, D. , Camayo, B. and Mucha, E. (2020) Assessment of Wind Power Density Based on Weibull Distribution in Region of Junin, Peru. Energy and Power Engineering, 12, 16-27. doi: 10.4236/epe.2020.121002.
References

[1]   REN21. Renewables Global Futures Report: Great Debates towards 100% Renewable Energy.
https://www.ren21.net/reports/global-futures-report

[2]   IRENA. Renewable Energy Statistics 2019.
https://www.irena.org/Statistics/View-Data-by-Topic/Capacity-and-Generation/Regional-Trends

[3]   Weisser, D. (2003) A Wind Energy Analysis of Grenada: An Estimation Using the “Weibull” Density Function. Renewable Energy, 28, 1803-1812.
https://doi.org/10.1016/S0960-1481(03)00016-8

[4]   Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N. and Jalilvand, M. (2016) Assessing Different Parameters Estimation Methods of Weibull Distribution to Compute Wind Power Density. Energy Conversion and Management, 108, 322-335.
https://doi.org/10.1016/j.enconman.2015.11.015

[5]   Lu, L., Yang, H.X. and Burnett, J. (2002) Investigation on Wind Power Potential on Hong Kong Islands—An Analysis of Wind Power and Wind Turbine Characteristics. Renewable Energy, 27, 1-12.
https://doi.org/10.1016/S0960-1481(01)00164-1

[6]   Seguro, J.V. and Lambert, T.W. (2000) Modern Estimation of the Parameters of the Weibull Wind Speed Distribution for Wind Energy Analysis. Journal of Wind Engineering and Industrial Aerodynamics, 85, 75-84.
https://doi.org/10.1016/S0167-6105(99)00122-1

[7]   Persaud, S., Flynn, D. and Fox, B. (1999) Potential for Wind Generation on the Guyana Coastlands. Renewable Energy, 18, 175-189.
https://doi.org/10.1016/S0960-1481(98)00793-9

[8]   De, A.R. and Musgrove, L. (1988) The Optimization of Hybrid Energy Conversion Systems Using the Dynamic Programming Model—Rapsody. International Journal of Energy Research, 12, 447-457.
https://doi.org/10.1002/er.4440120309

[9]   Katinas, V., Marciukaitis, M., Gecevicius, G. and Markevicius, A. (2017) Statistical Analysis of Wind Characteristics Based on Weibull Methods for Estimation of Power Generation in Lithuania. Renewable Energy, 113, 190-201.
https://doi.org/10.1016/j.renene.2017.05.071

[10]   Brano, V.L., Orioli, A., Ciulla, G. and Culotta, S. (2011) Quality of Wind Speed Fitting Distributions for the Urban Area of Palermo, Italy. Renewable Energy, 36, 1026-1039.
https://doi.org/10.1016/j.renene.2010.09.009

[11]   Aukitino, T., Khan, M.G.M. and Ahmed, M.R. (2017) Wind Energy Resource Assessment for Kiribati with a Comparison of Different Methods of Determining Weibull Parameters. Energy Conversion and Management, 151, 641-660.
https://doi.org/10.1016/j.enconman.2017.09.027

[12]   Fyrippis, I., Axaopoulos, P.J. and Panayiotou, G. (2010) Wind Energy Potential Assessment in Naxos Island, Greece. Applied Energy, 87, 577-586.
https://doi.org/10.1016/j.apenergy.2009.05.031

[13]   Islam, M.R., Saidur, R. and Rahim, N.A. (2011) Assessment of Wind Energy Potentiality at Kudat and Labuan, Malaysia Using Weibull Distribution Function. Energy, 36, 985-992.
https://doi.org/10.1016/j.energy.2010.12.011

[14]   Celik, A.N. (2004) A Statistical Analysis of Wind Power Density Based on the Weibull and Rayleigh Models at the Southern Region of Turkey. Renewable Energy, 29, 593-604.
https://doi.org/10.1016/j.renene.2003.07.002

[15]   Ali, S., Lee, S.-M. and Jang, C.-M. (2018) Statistical Analysis of Wind Characteristics Using Weibull and Rayleigh Distributions in Deokjeok-Do Island-Incheon, South Korea. Renewable Energy, 123, 652-663.
https://doi.org/10.1016/j.renene.2018.02.087

[16]   Khahro, S.F., Tabbassum, K., Soomro, A.M., Dong, L. and Liao, X.Z. (2014) Evaluation of Wind Power Production Prospective and Weibull Parameter Estimation Methods for Babaurband, Sindh Pakistan. Energy Conversion and Management, 78, 956-967.
https://doi.org/10.1016/j.enconman.2013.06.062

[17]   Ahmed, S.A. (2013) Comparative Study of Four Methods for Estimating Weibull Parameters for Halabja, Iraq. International Journal of Physical Sciences, 8, 186-192.

[18]   Mohammadi, K. and Mostafaeipour, A. (2013) Using Different Methods for Comprehensive Study of Wind Turbine Utilization in Zarrineh, Iran. Energy Conversion and Management, 65, 463-470.
https://doi.org/10.1016/j.enconman.2012.09.004

[19]   Chang, T.P. (2011) Performance Comparison of Six Numerical Methods in Estimating Weibull Parameters for Wind Energy Application. Applied Energy, 88, 272-282.
https://doi.org/10.1016/j.apenergy.2010.06.018

[20]   Justus, C.G., Hargraves, W.R. and Yalcin, A. (1976) Nationwide Assessment of Potential Output from Windpowered Generators. Journal of Applied Meteorology, 15, 673-678.
https://doi.org/10.1175/1520-0450(1976)015<0673:NAOPOF>2.0.CO;2

[21]   Lysen, E.H. (1983) Introduction to Wind Energy. Consultancy Services Wind Energy Developing Countries.

[22]   Li, M.-F., Tang, X.-P., Wu, W. and Liu, H.-B. (2013) General Models for Estimating Daily Global Solar Radiation for Different Solar Radiation Zones in Mainland China. Energy Conversion and Management, 70, 139-148.
https://doi.org/10.1016/j.enconman.2013.03.004

[23]   Legates, D.R. and McCabe Jr., G.J. (1999) Evaluating the Use of “Goodness-of-Fit” Measures in Hydrologic and Hydroclimatic Model Validation. Water Resources Research, 35, 233-241.
https://doi.org/10.1029/1998WR900018

[24]   Willmott, C.J. (1981) On the Validation of Models. Physical Geography, 2, 184-194.
https://doi.org/10.1080/02723646.1981.10642213

[25]   Jamieson, P.D., Porter, J.R. and Wilson, D.R. (1991) A Test of the Computer Simulation Model ARCWHEAT1 on Wheat Crops Grown in New Zealand. Field Crops Research, 27, 337-350.
https://doi.org/10.1016/0378-4290(91)90040-3

 
 
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