Short-Term Load Forecasting Based on Wavelet Neural Network and Monkey-King Genetic Algorithm

This paper presents a short-term load forecasting method based on wavelet neural network (WNN) and monkey-king genetic algorithm (MK). Parameters of WNN are mostly selected artificially or obtained through experiment time after time. A certain and effective method has not been found. Aiming at solving this problems, a method optimizing the WNN parameters with monkey-king genetic algorithm (MKWNN) was presented. The simulation results show that the proposed method possesses high forecasting accuracy and adaptability.