Load Forecasting Based on Chaotic Support Vector Machine with Incorporated Intelligence Algorithm

Accurate power load forecasting is important for electric power system, for it guarantees its economical and safe operation. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. According to the chaotic and nonlinear characters analyze of power load data, the model of support vector machines (SVM) based on Lyapunov exponents was established. The time series matrix was established according to the theory of phase-space reconstruction, and then Lyapunov exponents was computed to determine time delay and embedding dimension. A new incorporated intelligence algorithm is proposed and used to determine free parameters of support vector machines. Subsequently, examples of electricity load data from a city in inner Mongolia autonomous region. The empirical results reveal that the proposed model outperforms the SVM model, BP algorithm was used to compare with the result of SVM. The results show that the presented method is feasible and effective