Research on photovoltaic ultra short-term power prediction algorithm based on attention and LSTM. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Yan, Aiyun; Gu, Jinbo; Mu, Yahui; Li, Jingjiao; Jin, Shuowei; Wang, Aixia
• Is Part Of: IOP conference series. Volume 675:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/675/1/012078
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Based on the actual monitoring historical data of photovoltaic power station, combined with the actual engineering demand of photovoltaic microgrid on the user side, the lightweight algorithm of ultra short-term photovoltaic power prediction is studied, which is conducive to improving the operation efficiency and economy of power system. In this paper, the ultra short-term power prediction of photovoltaic power station is carried out by combining the LSTM algorithm with attention mechanism. Firstly, Pearson correlation coefficient method is used to reduce the dimension of the data set. The data with low correlation between weather variables and power to be predicted and historical power are eliminated, and the algorithm model structure is simplified. Then, the attention mechanism is combined with LSTM network to improve the effectiveness of the prediction model for long time series input. The proposed model is trained and compared with the data of a photovoltaic power station. The results show that the model achieves good experimental results in different weather conditions, and can effectively improve the prediction accuracy.