Short-Term Electricity Price Forecasting Based on PSO Algorithm and RBF Neural Network Algorithm

摘要：

A method of Radial Basis Function(RBF)neural network algorithm based on Particle Swarm Optimization (PSO) algorithm is introduced.In the background of PJM electricity market in the USA, the short-term price is forecasted with the historical price and loads.After determining the number, the center and width of the hidden layer ,code the weights of output layer to individual particles and optimize them,then search the weight value of the best in the overall space.The result says that the new algorithm can improve the accuracy compared the traditional RBF network forcasting methods, so it has good application prospect.

关键词： component elctricity system particle swarm optimization RBF neural network short-term eletrcity price forecast

作者: Zhang Caiqing Ma Peiyu

作者单位: North China Electric Power University Department of Economic Management BaoDing,China

会议类型: 国际会议

会议名称: 2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 2588-2591

在线出版日期: 2010-03-13（万方平台首次上网日期，不代表论文的发表时间）