Application Study On Short-term Load Forecasting Based On Support Vector Regression

Short-term load forecasting has become increasingly important since the rise of the competitive energy markets and has become one of the major areas of research in recent years. Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. Today more and more papers applied support vector machines in short-term load forecasting and get good results. This paper did study on Short-term load forecasting,Support Vector Machines(SVM),Sequential Minimal Optimization(SMO) theory and did experiments on the load data by using linear regression, SVR and SMO algorithms. The results show that SMO algorithm has better adaptability and excellent forecasting accuracy.