Short-term photovoltaic power forecasting based on fluctuation characteristics mining

被引：0
|
作者：
Ji X. [1 ]
Li H. [1 ]
Ye L. [2 ]
Wang L. [1 ]
机构：
[1] College of Automation, Beijing Information Science and Technology University, Beijing
[2] College of Information and Electrical Engineering, China Agricultural University, Beijing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 05期
关键词：
Data mining; Deep learning; Fluctuations; Information entropy; Photovoltaic power generation; Power forecasting;
D O I：
10.19912/j.0254-0096.tynxb.2020-0961
中图分类号：
学科分类号：
摘要：
A short-term photovoltaic power forecasting method based on fluctuation characteristics mining is proposed in this paper. Firstly, the classification method and cluster identification method of photovoltaic power fluctuation are presented, considering the regularity and volatility of photovoltaic power affected by meteorological factors. Secondly, the Numerical Weather Prediction and the correlation analysis based on mutual information entropy are used to extract the weather fluctuation characteristics and highly correlated meteorological factors corresponding to various power fluctuations. Thirdly, the combined model of the long-short term memory network is put forward to mine the potential mapping relationship between the weather fluctuation and photovoltaic power fluctuation. Finally, after the types of weather fluctuations on the tested day are identified, its photovoltaic powers are predicted by using the combined method. The results of a photovoltaic power station in Northwest China show that the proposed model is effective. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：146 / 155
页数：9
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