Research on Photovoltaic Power Generation Power Prediction Based on LSTM Network
HE Qipeng
SUN Mingyang
YANG Miaomiao
XU Liansheng
LI Jian
HE Xiaoyang
Abstract：With the global transition to new energy sources,the demand for clean energy is continuously increasing.Photovoltaic(PV)power generation,as an important component of this shift,has its power prediction accuracy crucial for the stable operation of the power grid and the allocation of spare capacity.Traditional prediction models based on physical models,similar day methods,and statistical methods suffer from poor robustness and weak adaptability due to their lack of historical memory capabilities.To address these issues,this paper proposes a short-term prediction method for PV power generation power based on Long Short-Term Memory(LSTM)networks.In the preprocessing stage,this paper first cleans the bad and discrete points in the obtained data;it then uses the Spearman correlation coefficient to analyze the correlation between PV power generation power and environmental impact factors,obtaining the input variables.A short-term prediction model for PV power generation power based on LSTM networks is established,and its significant advantages in dynamic capture of time series data and prediction accuracy are verified by comparison with BP and decision tree models.The prediction results provided by the LSTM model are accurate and reliable,effectively assisting in power dispatch decision-making.
Keywords：photovoltaic power generation power predictionLSTMspearman correlation coefficient
Publication Date：2025-02-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 72-79 )