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 (SVM) technology for the short-term electricity load forecast. 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.