Optimizing direct normal irradiance prediction of BP neural network based on Logistic Sparrow search algorithm

Abstract

With the wide application of photothermal power stations in power systems, Direct Normal Irradiance(DNI) prediction is very important to improve the economic benefits of photothermal power stations and the utilization of solar energy. The short-term prediction of wind power by traditional Sparrow search algorithm (SSA) optimized BP(SSA-BP) neural network is prone to fall into local optimum, slow convergence rate, and low prediction accuracy. A short-term DNI prediction method based on the Logistic Sparrow search algorithm (Logistic-SSA) optimized BP(Logistic-SSA-BP) neural network is proposed. This paper takes a photothermal power station in Northwest China as the research object. Firstly, the Pearson correlation coefficient is introduced to analyze the environmental data set with a strong correlation with the direct illumination radiation index as the input of the model, so as to avoid redundant data affecting the prediction of the direct illumination radiation index. Secondly, the SSA algorithm and Logistic-SSA algorithm are used to improve BP neural network. Finally, the measured historical data of the photothermal power station is used to simulate the prediction models. The simulation results show that the prediction model based on Logistic-SSA-BP has better prediction accuracy than SSA-BP, and is more in line with the production and operation requirements of the photothermal power station.