Hybrid evolutionary algorithms in a SVR-based electric load forecasting model

Accurately electric load forecasting has become the most important issue in energy management; however, electric load often presents nonlinear data patterns. Therefore, looking for a novel forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) reveals superior nonlinear modeling capabilities by applying the structural risk minimization principle to minimize an upper bound of the generalization errors, it is quite different with ANNs model that minimizing the training errors. The purpose of this paper is to present a SVR model with a hybrid evolutionary algorithm (chaotic genetic algorithm, CGA) to forecast the electric loads, CGA is applied to the parameter determine of SVR model. With the increase of the complexity and the larger problem scale of electric loads, genetic algorithms (GAs) are often faced with the problems of premature convergence, slowly reaching the global optimal solution or trapping into a local optimum. The proposed CGA based on the chaos optimization algorithm and GAs, which employs internal randomness of chaos iterations, is used to overcome premature local optimum in determining three parameters of a SVR model. The empirical results indicate that the SVR model with CGA (SVRCGA) results in better forecasting performance than the other methods, namely SVMG (SVM model with GAs), regression model, and ANN model.