A short-term load forecasting approach based on support vector machine with adaptive particle swarm optimization algorithm

Abstract

Aiming at the precocious convergence problem of particle swarm optimization algorithm, adaptive particle swarm optimization (APSO) algorithm was presented. In this algorithm, the notion of species was introduced into population diversity measure. The species technique is based on the concept of dividing the population into several species according to their similarity. The inertia weight was nonlinearly adjusted by using population diversity information at each iteration step. Velocity mutation operator and position crossover operator were both introduced and the global performance was clearly improved. The APSO algorithm was adapted to search the optimal parameters of support vector machine (SVM) to increase the accuracy of SVM. A novel short-term load forecasting model based on SVM with APSO algorithm (APSO-SVM) is presented. The proposed model was tested on a certain electricity load forecasting problem. The empirical results illustrated that the new APSO-SVM model outperformed SVM, BPNN and regression model and can successfully identify the optimal values of parameters of SVM with the lowest prediction error values in load forecasting. Therefore, this model is efficient and practical during a short-term load forecasting of electric power system.