文章基本信息

标题：A Wind Power Prediction Method Based on Bayesian Fusion
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作者：Jianqi An ; Zhangbing Chen ; Min Wu 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2017
卷号：7
期号：3
页码：27-37
DOI：10.5121/csit.2017.70303
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Wind power prediction (WPP) is of great importance to the safety of the power grid and theeffectiveness of power generations dispatching. However, the accuracy of WPP obtained bysingle numerical weather prediction (NWP) is difficult to satisfy the demands of the powersystem. In this research, we proposed a WPP method based on Bayesian fusion and multisourceNWPs. First, the statistic characteristics of the forecasted wind speed of each-sourceNWP was analysed, pre-processed and transformed. Then, a fusion method based on Bayesianmethod was designed to forecast the wind speed by using the multi-source NWPs, which is moreaccurate than any original forecasted wind speed of each-source NWP. Finally, the neuralnetwork method was employed to predict the wind power with the wind speed forecasted byBayesian method. The experimental results demonstrate that the accuracy of the forecastedwind speed and wind power prediction is improved significantly.
关键词：Wind Power Prediction; Numerical Weather Prediction; Bayesian Fusion; Wind Speed Prediction