Study on a Novel Short-Term Load Forecasting Method Based on Improved PSO and FRBFNN

Abstract

In order to accurately, fast and efficiently forecast the short-term load of power system, an improved particle swarm optimization algorithm is proposed to optimize the parameters of fuzzy radial basis function fuzzy neural network(FRBFNN) model in order to train the FRBFNN model for obtaining the optimized FRBFNN(IWPSRFN) method. In the proposed IWPSRFN method, the linear decreasing weight method is used to adjust the inertia weight of PSO algorithm. The global optimization ability of improved PSO algorithm is used to adjust the parameters of FRBFNN model by putting these parameters in the particle encoding, then the optimal values are found in the large number of viable solutions by continuous iteration of improved PSO algorithm. The found optimal values are regarded as the parameters of FRBFNN model to obtain the final IWPSRFN method for forecasting short-term load of power system. Finally, a certain region is selected to test the effectiveness of IWPSRFN method, the experiment results show that the improved PSO algorithm can effectively optimize the weights of FRBFNN and solve the slow convergence speed, and the IWPSRFN method can obtain the higher prediction accuracy and is an effective method for forecasting short-term load.