Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions

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• 作者：Haiyan Jiang^a ; Jianzhou Wang^b ; ^{wjz@lzu.edu.cn} ; Jie Wu^c ; Wei Geng^a
• 关键词：PDF ; probability density function ; NM ; numerical methods ; MOA ; metaheuristic optimization algorithm ; MEP ; maximum entropy principle ; MCP ; Measure-Correlate-Predict ; GWF ; Gamma&ndash ; Weibull function ; MTN ; mixture truncated normal function ; GM ; graphical method ; MM ; method of moment ; EM ; empirical method ; LSM ; least&ndash ; squares method ; MLE ; maximum likelihood estimation ; MMLE ; modified maximum likelihood estimation method ; EPF ; energy pattern factor method ; EEM ; equivalent energy method ; PSO ; p
• 刊名：Renewable and Sustainable Energy Reviews
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：69
• 期：Complete
• 页码：1199-1217
• 全文大小：1513 K
• 卷排序：69

文摘

The low-speed wind energy potential in Yangtze River Delta City Group in China is assessed. BA, CS and PSO are employed for tuning the optimal parameters of distributions. Numerical methods and metaheuristic algorithms are compared. MM is the best numerical methods, however BA, CS and PSO are better than MM. BA-Weibull and PSO-Weibull perform the best among the distributions.