Research on Prediction Model of Photovoltaic Power Generation Based on Nonlinear Regression Based on BP Neural Network

Meteorological factors will have a certain impact on the output characteristics of the optoelectronic system. This is an unstable random signal. This paper uses a post-a priori prediction algorithm to classify it. In this way, a more accurate simulated climate can be obtained. In this method, particle swarm algorithm is used to optimally solve the correlation weights and thresholds in the neural network. The method proposed in this paper takes into account the advantages of BP neural network and particle swarm at the same time. It has achieved good results in terms of convergence rate, generalization and prediction accuracy. Experiments show that the PSO algorithm is used to predict the BP neural network, and its prediction accuracy is higher than that of the traditional BP neural network.