A Hybrid Model for Short-Term Load Forecasting Based on Non- Parametric Error Correction

In this paper, we presented the performance of forecasting model and error correction will affect the accuracy of short-term load forecasting. Least squares support vector machines (LS-SVM) based on improved particle swarm optimization is selected as load forecasting model. Forecasting accuracy and generalization performance of LS-SVM depend on selection of its parameters greatly. Adaptive particle swarm optimization (APSO) based on fitness function was put forward to optimize the kernel parameter σ and regularization parameter γ of LS-SVM. Based on the optimized forecasting model, nonparametric error correction model is also presented by iterative method. The error forecasted by non-parametric model was used to update the forecasted load so as to improve the forecasting accuracy. Load data selected from some area in South China as training and forecasting data is used to analyze. Case study illustrates that the proposed forecasting model (NP-APSO-SVM) has more generalized performance and better forecasting accuracy compared with the method of standard SVM.