Novel approach considering load-relative factors in short-term load forecasting

Abstract

With structural changes to electricity industry in recent years, there is an increasing emphasis on short-term load forecasting (STLF). STLF is the essential part of power system planning and operation. In deregulated environment, STLF greatly affects system marginal price. Therefore many kinds of models for STLF have been proposed in recent 30 years. However, an individual forecasting model cannot work well in every possible condition. To overcome this problem, an integrated model, based on the idea of simulative forecasting, which can synthesize individual forecasting model results, is proposed in this paper. In practice, STLF is always affected by a variety of nonlinear factors. Since it is difficult to identify the nonlinearity by conventional methods, a novel approach is proposed which is able to deal with most factors affecting power load. The major idea in the approach is to define a mapping function for each factor. Finally, an algorithm for optimizing mapping functions is presented. Case studies show that it is a successful solution to improve the accuracy of STLF.