Short-term Power Load Forecasting Based on Improved BP Neural Network from Genetic Algorithm and Simulated Annealing Algorithm

Abstract

As one of the common methods of load forecasting, the traditional BP neural network algorithm has been favored by scholars for many years due to its strong learning ability and adaptive ability, and is often used by scholars to forecast power load. However, BP neural network also has its own shortcomings, that is, low algorithm efficiency, local minimization and other problems. In this paper, GA algorithm and SA algorithm are used to modify the weights of BP neural network, and a short-term load forecasting model, namely BP-GSA model, is established. The least mean square error prediction model is obtained through training. Then using BP-GSA model and traditional BP neural network model, using MATLAB software to predict the daily power load of a city from July 1 to July 10. The forecast results show that the load forecast curve obtained by BP-GSA model is closer to the actual load curve. That is to say, BP-GSA load forecasting error is less than the traditional BP neural network load forecasting error, which proves that BP-GSA model has better forecasting effect.