Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of T...

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Autores principales:	Ahmet Emre Onay, Emrah Dokur, Mehmet Kurban
Formato:	article
Lenguaje:	EN
Publicado:	Kaunas University of Technology 2021
Materias:	wind energy estimation methods weibull distribution wind energy intensification Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/50a19941a72647eba7948aa1e0c6d014
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spelling	oai:doaj.org-article:50a19941a72647eba7948aa1e0c6d0142021-11-04T14:14:15ZPerformance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density1392-12152029-573110.5755/j02.eie.28919https://doaj.org/article/50a19941a72647eba7948aa1e0c6d0142021-10-01T00:00:00Zhttps://eejournal.ktu.lt/index.php/elt/article/view/28919https://doaj.org/toc/1392-1215https://doaj.org/toc/2029-5731To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.Ahmet Emre OnayEmrah DokurMehmet KurbanKaunas University of Technologyarticlewind energyestimation methodsweibull distributionwind energy intensificationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElektronika ir Elektrotechnika, Vol 27, Iss 5, Pp 41-48 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wind energy estimation methods weibull distribution wind energy intensification Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	wind energy estimation methods weibull distribution wind energy intensification Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ahmet Emre Onay Emrah Dokur Mehmet Kurban Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density
description	To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.
format	article
author	Ahmet Emre Onay Emrah Dokur Mehmet Kurban
author_facet	Ahmet Emre Onay Emrah Dokur Mehmet Kurban
author_sort	Ahmet Emre Onay
title	Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density
title_short	Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density
title_full	Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density
title_fullStr	Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density
title_full_unstemmed	Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density
title_sort	performance comparison of new generation parameter estimation methods for weibull distribution to compute wind energy density
publisher	Kaunas University of Technology
publishDate	2021
url	https://doaj.org/article/50a19941a72647eba7948aa1e0c6d014
work_keys_str_mv	AT ahmetemreonay performancecomparisonofnewgenerationparameterestimationmethodsforweibulldistributiontocomputewindenergydensity AT emrahdokur performancecomparisonofnewgenerationparameterestimationmethodsforweibulldistributiontocomputewindenergydensity AT mehmetkurban performancecomparisonofnewgenerationparameterestimationmethodsforweibulldistributiontocomputewindenergydensity
_version_	1718444837908250624

Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

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