A Hybrid Particle Swarm Optimization Neural Network Approach for Short Term Load Forecasting

Short term load forecasting (STLF) plays a significant role in national/regional power planning and operation with insufficient electric energy increased need. The accuracy of the operation system, which is derived from the accuracy of the forecasting approach used, will determine the economics of the operation of the power system. Conventional methods including time series, regression analysis or ARMA model entail exogenous input together with a number of assumptions. The use of neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with some drawbacks such as very slow convergence and easy entrapment in a local minimum. This paper presents a hybrid approach of neural network with particle swarm optimization training algorithm for developing the accuracy of predictions. The approach is applied to forecast daily peak loads (maximum of load during the day) of the Beibei, Chongqing electricity system based on previous data available for electricity demand. Traditional ARMA model and BP neural network are investigated as comparison basis. The experimental results show that the proposed approach can achieve better prediction performance.