globalchange  > 过去全球变化的重建
DOI: 10.1007/s00382-015-2682-2
Scopus记录号: 2-s2.0-84959098801
论文题名:
Application of extreme learning machine for estimation of wind speed distribution
作者: Shamshirband S.; Mohammadi K.; Tong C.W.; Petković D.; Porcu E.; Mostafaeipour A.; Ch S.; Sedaghat A.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 46, 期:2017-05-06
起始页码: 1893
结束页码: 1907
语种: 英语
英文关键词: Extreme learning machine (ELM) ; Scale factor ; Shape factor ; Weibull function ; Wind speed distribution
英文摘要: The knowledge of the probabilistic wind speed distribution is of particular significance in reliable evaluation of the wind energy potential and effective adoption of site specific wind turbines. Among all proposed probability density functions, the two-parameter Weibull function has been extensively endorsed and utilized to model wind speeds and express wind speed distribution in various locations. In this research work, extreme learning machine (ELM) is employed to compute the shape (k) and scale (c) factors of Weibull distribution function. The developed ELM model is trained and tested based upon two widely successful methods used to estimate k and c parameters. The efficiency and accuracy of ELM is compared against support vector machine, artificial neural network and genetic programming for estimating the same Weibull parameters. The survey results reveal that applying ELM approach is eventuated in attaining further precision for estimation of both Weibull parameters compared to other methods evaluated. Mean absolute percentage error, mean absolute bias error and root mean square error for k are 8.4600 %, 0.1783 and 0.2371, while for c are 0.2143 %, 0.0118 and 0.0192 m/s, respectively. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate Weibull k and c factors. © 2015, Springer-Verlag Berlin Heidelberg.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53779
Appears in Collections:过去全球变化的重建

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作者单位: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala lumpur, Malaysia; Faculty of Mechanical Engineering, University of Kashan, Kashan, Iran; Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Technical University Federico Santa Maria, Valparaíso, Chile; Industrial Engineering Department, Yazd University, Yazd, Iran; Department of Civil and Environmental Engineering, ITM University, Gurugaon, India; Department of Mechanical Engineering, School of Engineering, Australian College of Kuwait, P.O. Box 1411, Safat, Kuwait

Recommended Citation:
Shamshirband S.,Mohammadi K.,Tong C.W.,et al. Application of extreme learning machine for estimation of wind speed distribution[J]. Climate Dynamics,2016-01-01,46(2017-05-06)
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