Short-Term Photovoltaic Power Prediction Algorithm Based on Improved BP Neural Network

Abstract

At present, photovoltaic power generation is becoming more and more important in the field of new energy power generation. The traditional BP neural network algorithm is prone to fall into local extreme points in the training process of the short-term photovoltaic power prediction model, and the training model If the global optimum is not reached, the optimization is stopped, and the weights of the initial network are randomly selected, which leads to problems such as low model prediction accuracy and low iteration efficiency. This paper proposes a short-term photovoltaic power prediction algorithm based on improved BP neural network. The method uses the Levenberg-Marquardt algorithm to replace the traditional gradient descent method for training, which improves the iterative speed of the traditional BP neural network training, and uses the genetic algorithm to optimize the initial weight of the network model, which further improves the model prediction accuracy. The simulation results show that the prediction accuracy of the improved algorithm for short-term photovoltaic power is significantly better than that of the algorithm before the improvement, and the stability and iterative efficiency are greatly improved.