Electricity price forecasting based on support vector machine trained by genetic algorithm

Authors:
Chen Yan-Gao;Ma Guangwen
Affiliations:
College of Water Resource and Hydropower Institute, Sichuan University, Chengdu, China;College of Water Resource and Hydropower Institute, Sichuan University, Chengdu, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

Accurate electricity price forecasting can provide crucial information for electricity market participants to make reasonable competing strategies. Support vector machine (SVM) is a novel algorithm based on statistical learning theory, which has greater generalization ability, and is superior to the empirical risk minimization principle as adopted by traditional neural networks. However, its generalization performance depends on a good setting of the training parameters C, σ Ɛ for the nonlinear SVM. In the study, support vector machine trained by genetic algorithm (GA-SVM) is adopted to forecast electricity price, in which GA is used to select parameters of SVM. National electricity price data in China from 1996 to 2007 are used to study the forecasting performance of the GASVM model. The experimental results show that GA-SVM algorithm has better prediction accuracy than radial basis function neural network (RBFNN).