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Electric load forecasting by support vector model

Authors
Journal
Applied Mathematical Modelling
0307-904X
Publisher
Elsevier
Publication Date
Volume
33
Issue
5
Identifiers
DOI: 10.1016/j.apm.2008.07.010
Keywords
  • Support Vector Regression (Svr)
  • Immune Algorithm (Ia)
  • Electric Load Forecasting
Disciplines
  • Computer Science

Abstract

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.

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