Short-Term Load Forecasting Model Based on LS-SVM in Bayesian Inference

Abstract

Short-term load forecasting is very important for power system. A combined excellent model based on least squares support vector machines in bayesian inference is proposed in this paper to do the short-term load forecasting. Least squares support vector machines (LS-SVM) are new kinds of support vector machines (SVM) which regress faster than the standard SVM, they are adopt to do the forecasting, and the parameters of model proposed are gained in the three levels of bayesian inference. A real case is experimented with to test the performance of the model, the result shows that the proposed combined model outperforms BP network which is choose to be the comparative model, so improving the accuracy of load forecasting.