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Electric load forecasting by support vector model
Authors:Wei-Chiang Hong
Institution:Department of Information Management, Oriental Institute of Technology 58, Section 2, Sichuan Road, Panchiao, Taipei County 220, Taiwan
Abstract:Accurately electric load forecasting has become the most important management goal, however, electric load often presents nonlinear data patterns. Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization errors, rather than minimizing the training errors which are used by ANNs. The purpose of this paper is to present a SVR model with immune algorithm (IA) to forecast the electric loads, IA is applied to the parameter determine of SVR model. The empirical results indicate that the SVR model with IA (SVRIA) results in better forecasting performance than the other methods, namely SVMG, regression model, and ANN model.
Keywords:Support vector regression (SVR)  Immune algorithm (IA)  Electric load forecasting
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