Combined regression-fuzzy approach for short-term load forecasting

Abstract

Accurate load forecasting is of great importance for power system operation; it is the basis of economic dispatch, unit commitment, hydrothermal coordination, and system security analysis, among other functions. An approach based on combined regression method and fuzzy inference system is developed for short-term load forecasting. The multilinear regression model is applied to find a preliminary load forecast. In addition, the fuzzy inference system makes a load correction inference from historical information and past forecast load errors from a multilinear regression model to infer a forecast load error. Adding the inferred load error to the preliminary load forecast obtains a final forecast load. The effectiveness of the proposed approach to the short-term load forecasting problem is demonstrated by practical data from the Taiwan Power Company.