Title :
Short-Term Electricity Price Forecasting Based on PSO Algorithm and RBF Neural Network Algorithm
Author :
Zhang Caiqing ; Ma Peiyu
Author_Institution :
Dept. of Economic Manage., North China Electr. Power Univ., BaoDing, China
Abstract :
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.
Keywords :
electricity supply industry; forecasting theory; particle swarm optimisation; power generation economics; power markets; pricing; radial basis function networks; PJM electricity market; PSO; RBF neural network; USA; particle swarm optimization; radial basis function neural network; short term electricity price forecasting; Artificial neural networks; Conference management; Economic forecasting; Energy management; Load forecasting; Neural networks; Particle swarm optimization; Power generation economics; Radial basis function networks; Technology forecasting; RBF neural network; elctricity system; particle swarm optimization; short-term eletrcity price forecast;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
DOI :
10.1109/ICMTMA.2010.22
