SHORT TERM LOAD FORECASTING BASED ON BP NEURAL NETWORK TRAINED BY PSO

摘要

A short-term load forecasting method based on BP neural network which is optimized by particle swarm optimization (PSO) algorithm is presented in this paper.PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization.Here, real load and weather data from the Xingtai power plant databases used as inputs to the neural network, which has been trained by PSO, are employed to illustrate the presented model.The experimental results prove that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional BP method and show that the method is not only simple to calculate, but also practical and effective.