Research on short-term load forecasting based on adaptive hybrid genetic optimization BP neural network algorithm

Abstract

Short-term load forecasting may impact the plan of the production of the electricity system and the arrangement of the power system short-term operation mode, and it has great economic significance. Some scholars used BP neural network to forecast the short-term load, and they found it has the intrinsic defects. Itpsilas difficult to determine the network structure and it easily run into partial minimum points. Based on genetic algorithmpsilas strong global searching ability and BP neural networkpsilas accurate local searching ability, this paper proposes an adaptive hybrid genetic BP neural network algorithm. It uses genetic algorithm to optimize the BP network initial weight first, then uses the BP neural network to learn by itself according to the data given, to acquire an excellent load forecasting system. In the training of neural network, the over-fitting often appears which affects the result of forecasting. To prevent this problem, the entire data set is divided into training set and validation set randomly. This algorithm was used to predict the load of Sydney. Simulation results indicate that the algorithm improves the forecast accuracy and the performance of the network.