Power load forecasting based on support vector machine and particle swarm optimization

Abstract

Accurate electric load forecasting is significant for the operation of the power systems and electricity markets. This paper proposes a particle swarm optimization with support vector machine (PSOSVM) to forecast annual power load. Based on radial basis function, support vector machine (SVM) is utilized to determine the structure and initial values of the parameters. Then, particle swarm optimization (PSO) is employed to optimize the parameters of the SVM model. In order to utilize the proposed method, practical data are divided into two parts, one is for training, the other is for testing. The combined method, PSOSVM, can effectively predict annual power load.