Photovoltaic power generation and charging load prediction research of integrated photovoltaic storage and charging station

Abstract

Aiming at the obvious randomness and intermittent problems of photovoltaic power generation output and charging load of photovoltaic storage and charging station, a photovoltaic power generation prediction model based on long short-term memory neural network and a charging load prediction model based on BP neural network are proposed respectively. In the photovoltaic power generation prediction model, the Pearson correlation coefficient is used to analyze the influencing factors of photovoltaic power generation power, and the factors with high correlation are used as the input variables of the long short-term memory neural network model. Then the data is divided into four types of seasons by K-means cluster analysis, and the processed data sets are put into the prediction model for training. In the charging load prediction model, the processed historical load data is put into the time series-based BP neural network for iterative training, meanwhile the weights and thresholds of the model are adaptively optimized. Finally, the simulation results verify the extremely high prediction accuracy of the proposed prediction model. The results also show that compared with PSO-BP, GA-BP neural network, this charging load prediction model has higher precision and stability.