Short Term Load Forecasting Based on the Particle Swarm Optimization with Simulated Annealing
This paper presented an artificial neural network (ANN) method based on the particle swarm optimization (PSO) and simulated annealing (SA) for load forecasting. It train the ANN network and facilitate the tuning of the optimal network weight and threshold by using the modified PSO with SA. The ANN network has a better ability to escape from the local optimum and is more effective than the conventional PSO-based ANN. Then forecast the daily load by using the network. Simulation example shows that the proposed approach has good accuracy.
short term load forecasting artificial neural network particle swarm optimization simulated annealing
LIU Chen LIU Fasheng
School of Information Science and Technology, Shandong University of Science and Technology, Qingdao School of Information Science and Electrical Engineering, Shandong University of Science and Technol
国际会议
威海
英文
140-144
2010-07-24(万方平台首次上网日期,不代表论文的发表时间)