Research on Distributed Photovoltaic Short-Term Power Prediction Method Based on Weather Fusion and LSTM-Net

doi:10.11930/j.issn.1004-9649.202104057

Abstract

Abstract: The high-precision prediction of distributed photovoltaic power generation is of great significance to the safe and stable operation of the distribution network. In this paper, based on weather information and depth learning method, a short-term power prediction method for distributed photovoltaic power generation equipment is proposed. First, classify and fuse the weather to achieve full coverage of the training set. Then, build a distributed photovoltaic short-term power prediction model based on the long short-term memory (LSTM) deep learning network. Finally, realize distributed photovoltaic power prediction.

Key words: distributed photovoltaic, photovoltaic power generation forecast, deep learning, short-term forecasting

LI Fengjun, WANG Lei, ZHAO Jian, ZHANG Jianbin, ZHANG Shiyao, TIAN Yangyang. Research on Distributed Photovoltaic Short-Term Power Prediction Method Based on Weather Fusion and LSTM-Net[J]. Electric Power, 2022, 55(11): 149-154.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202104057

https://www.electricpower.com.cn/EN/Y2022/V55/I11/149

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