A fuzzy inference model for short-term load forecasting

This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Forecasting is a significant element in economic system performance and its impact on network power control. Load forecasting with the uses of fuzzy implementation is faster and more accurate than conventional load forecasting methods that deal with huge amount of data and the long time needed to be processed. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. The proposed fuzzy-based STLF method is applied on a real case study, and the results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes.