Short-term load forecasting using comprehensive combination based on multi- meteorological information

Short-term load forecasting is always a popular topic in electric power industry because of its essentiality in energy system planning and operation. In the deregulated power system, the increase for a few percentages in the prediction accuracy would bring benefits worth of millions of dollars, which makes load forecasting become more important than ever before. This paper focuses on the short-term load forecasting for a power system in the United States, where several alternative meteorological forecasts are available from different commercial weather services. To effectively make use of the alternative meteorological predictions to the load forecasting system, a new comprehensive forecasting methodology has been proposed in this paper. Specifically, combining forecasting using adaptive coefficients is applied to share the strength of the different temperature forecasts in the first stage, and then ensemble neural networks has been used to improve the model's generalization performance based on Bagging. The proposed load forecasting system has been verified by using the real data from a utility. A range of comparisons with different forecasting models have been conducted. The forecasting results demonstrate the superiority of the proposed methodology.