 Monthly wind distribution prediction based on nonparametric estimation and modified differential evolution optimization algorithmLing Liu, Jujie Wang, Jianping Li and Lu Wei Renewable Energy, 2023, vol. 217, issue C Abstract: To copy with global warming and increasing energy demand, wind power has been rapidly developed around the world in recent years. It is important to analyse the characteristics of wind speed distribution to improve the development and utilization of wind energy. Many studies focus on improving the estimation accuracy of wind speed distribution, but there are few studies on variation characteristics and predictability of it. Here, we present a novel horizontal-vertical-integration framework to predict wind speed distribution. To address the predictability problem of nonparametric estimation, we proposed a data sampling and mapping method. To indirectly optimize the learning rate of the hybrid neural network, an improved differential evolution optimization algorithm was designed. The effectiveness of the proposed methods was verified by using data of 9 wind stations and 6 comparison models. The results show that the absolute error of the proposed prediction framework is less than 0.0059, which is better than other comparison models. Keywords: Distribution prediction; Kernel density estimation; Differential evolution optimization (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:217:y:2023:i:c:s0960148123010133 DOI: 10.1016/j.renene.2023.119099 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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