A fuzzy approach for Khorasan Razavi 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, unit commitment decisions and security assessment. Precise load forecasting plays an important role in reducing the generation cost and spinning reserve capacity. The inaccuracy in the forecast 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. It predicts the effect of temperature and historical data on load forecasting in terms of fuzzy sets during the generation process. Case studies have been carried out for the Khorasan Razavi consumption. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The simulation results show that the STLF of the fuzzy implementation have more accuracy and better outcomes than conventional approach.

کلیدواژه ها:

Fuzzy logic ، Load forecast ، STLF