An improved new approach for electric capacity forecasting based on historical data of GDP

Prediction is important for the electricity capacity management. Accurate prediction can help the policymaker make correct decision and promote the decision making quality. For improving an accuracy of prediction, in this paper, we adopt the theory of grey prediction to develop a new forecasting approach that integrates historical data of the gross domestic products (GDP) into an electric capacity forecasting. We adopted grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. As a result, our study considered that Wu and Chen proposed a modeling method of the improved grey relational analysis and main shows that the general grey model, GM (1,1), which is an especial case, is adequate to handle an electrical power system. In this study, the prediction is improved significantly by applying the transformed Grey model and the concept of average system slope. The adaptive value of a in the Grey differential equation is obtained quickly with the average system slope technique. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.