关键词: 光伏功率预测, CEEMD 算法, Pearson 相关矩阵, ISSA-LSTM 算法

Abstract: In order to better utilize solar energy, it is very important to accurately predict photovoltaic power generation. To improve the accuracy of photovoltaic power prediction,a photovoltaic power prediction method based on the combination of factor-related complementary ensemble empirical mode decomposition and optimized long short-term memory network is proposed. Firstly, the CEEMD(Complementary Ensemble Empirical Mode Decomposition) algorithm is used to decompose the photovoltaic power sequence, and the Pearson correlation coefficient matrix of the decomposed power components and environmental factors is established. Three key factors are selected as the input of the subsequent prediction for each decomposed power component.

Key words: photovoltaic power prediction, complementary ensemble empirical mode decomposition(CEEMD), Pearson correlation coefficient matrix, improved sparrow search algorithm-long short-term memory network(ISSA-LSTM)