The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM

Abstract

Electrical load forecasting plays a key role in energy scheduling and planning. It is a challenge to predict electric load accurately due to the versatility of electrical loads and the vast number of users in DSM of low-voltage side. Most of electrical load forecasting research focused on single model prediction or combination model prediction, which cannot get the optimal performance for some cases. Therefore, how to gather maximum optimal information from various different models is a key point in load forecasting and analysis. In this paper, an optimal selection method of multiple models for electrical load forecasting is studied. This method overcomes the shortcoming of unitary model, such as the instability and poor accuracy in some cases. To evaluate the forecast performance, a practical case is studied based on the intelligent electricity management system, which is presented by Wuhan University. It can be seen that the prediction error of the forecasting models can be calculated automatically and final optimum model can be obtained by optimum seeking software platform.