Short-term load forecasting model using fuzzy c Means based Radial Basis Function network

This paper presents the application of Fuzzy c Means based Radial Basis Function (RBF) network model to short term load forecasting problem. Traditional learning process for BP network is a nonlinear optimizing process, thus resulting in slow convergence speed, local minima. While the ability of approaching nonlinear function and convergence speed for RBF is superior to BP network. Before training network, suitable historical data were selected as training set through calculating difference degree function. This can make the training set representative, thus reduce training time. The proposed model has been implemented on real data: inputs to RFB are historical load value, weather, day and temperature information, and the output is the load forecast for the given hour. This model can effectively improve the speed of convergence. Using the presented model, the better forecasting accuracy and learning potency can be achieved.