A fuzzy inference model for short-term load forecasting

• Published in: Energy policy Vol. 37; no. 4; pp. 1239 - 1248
• Main Authors: Mamlook, Rustum, Badran, Omar, Abdulhadi, Emad
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-04-2009; Elsevier; Elsevier Science Ltd
• Series: Energy Policy
• Subjects: Accuracy; Applied sciences; Economic data; Electric energy; Electric utilities; Electricity; Electricity generation; Energy; Energy economics; Error; Exact sciences and technology; Forecasting; Forecasting techniques; Forecasts; Fuzzy logic; Fuzzy sets; Fuzzy sets Power generation Short-term load forecasting; General, economic and professional studies; Methodology. Modelling; Modelling; Parameter estimation; Power demand; Power generation; Short-term load forecasting; Studies; Jordan; Asia; Short-term load forecasting; Fuzzy sets; Power generation; Short term; Demand; Case study; Electricity; Energy economy; Inference; Fuzzy set; Modeling; Forecasting
• ISSN: 0301-4215; 1873-6777
• DOI: 10.1016/j.enpol.2008.10.051

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. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. 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. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes.