河北水利电力学院学报2026，Vol.36Issue(1)：33-38,6.DOI:10.16046/j.cnki.issn2096-5680.2026.01.006
基于SSA-LSTM神经网络的光伏发电量预测研究
Research on Photovoltaic Power Generation Prediction Based on SSA-LSTM Neural Network
摘要
Abstract
The prediction of photovoltaic power generation is influenced by many factors.To improve the accuracy of the prediction,statistical methods are first used to screen and statistically analyze experi-mental test data.In order to improve the accuracy of photovoltaic power generation prediction,environ-mental factors with high linear correlation with the power generation of the power station are selected and the SSA-LSTM neural network model are introduced for training and testing.By preprocessing historical data,the model firstly uses Singular Spectrum Analysis(SSA)to extract the main periodic components,and then utlizese Long Short Term Memory Networks(LSTM)for load prediction.The research results indicate that the model can effectively capture the dynamic changes in photovoltaic power generation,which has high accuracy in predicting photovoltaic power generation.关键词
光伏发电量预测/SSA-LSTM/环境因素Key words
photovoltaic power generation prediction/SSA-LSTM/environmental factor分类
信息技术与安全科学引用本文复制引用
刘志刚,刘涛,周玮,卜跃刚,杨昊..基于SSA-LSTM神经网络的光伏发电量预测研究[J].河北水利电力学院学报,2026,36(1):33-38,6.基金项目
河北省科技厅科技支撑计划项目(216Z5201G) （216Z5201G）
