Grey-RBF Neural Network Prediction Model for City Electricity Demand Forecasting

With the development of power markets, forecasting is becoming more and more important in such new competitive markets since the electricity demand forecasting is the basis of decision making for participants in electricity market. The aim of this project is to develop an electricity demand predictor. In this paper, we present an Grey-based prediction algorithm to forecast a long-term electric power demand for the demand-control of electricity. We adopted Grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. However, our preliminary study shows that the general Grey model, GM(1,1) is inadequate to handle a volatile electrical system. The general GM(1,1) prediction generates the dilemmas of dissipation and overshoots. Based on these influential factors, the corresponding RBF neural network forecasting model is presented. The proposed algorithm is more robust and reliable as compared to traditional approach and neural networks. In this study, the prediction is corrected significantly by applying the RBF neural network. The satisfactory results with better generalization capability and lower prediction error can be obtained. The present intelligent Grey-based electric demand-control system is able to provide an instrument to save operation costs for high energy consuming enterprises. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.