Power System Short -term Load Forecasting Based on Improved Support Vector Machines

Accurate forecasting of power system short -term load has been one of the most important issues in the electricity industry. And the forecasting accuracy is influenced by many unpredicted factors. Because of the non-linear features of short-term power load, the paper uses support vector machines(SV" technology for the short-term electricity loadforecast. The method can better solve such practical problems as small samples, nonlinearity, high dimensionality and local minimization, which can greatly enhancing its ability to handle non-linear. To solve the problems of SVM in training for large-scale convergence, such as slow convergence, greet complexity, particle swarm optimization (PSO) is proposed for the secondary planning problem to enhance SVM computing speed The improved SVM is applied to short-term load forecasting, empirical studies show that the method has a high prediction accuracy and faster computing speed.