Electricity Demand Forecasting Based on Feedforward Neural Network Training by a Novel Hybrid Evolutionary Algorithm

Electricity demand forecasting is an important index to make power development plan and dispatch the loading of generating units in order to meet system demand. In order to improve the accuracy of the forecasting, we apply the feedforward neural network for electricity demand forecasting. Inspired by the idea of artificial fish swarm algorithm, in this paper we proposed one hybrid evolutionary algorithm which based on PSO and AFSA methods through crossing over the PSO and AFSA algorithms to train the feedforward neural network. This proposed method has been applied in a real electricity load forecasting, the results show that the proposed approach has a better generalization performance and is also more accurate and effective than the feedforward neural network trained by particle swarm optimization.