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In designing fuzzy models to short-term load forecasting (STLF), we encounter a major difficulty in the identification of optimized fuzzy rule bases, which are traditionally obtained by trial and error. An approach to automatic design of optimal fuzzy rule bases using AEP (accelerated evolutionary programming) is proposed to construct the fuzzy models for short-term load forecasting. According to this approach, identification of the premise part and the consequence part is simultaneously accomplished, and the models complexity is also reduced compared to other fuzzy models. This method was tested on the Zhejiang Power Company's load data and the performances of the proposed method are compared to those of artificial neural network (ANN) models. The comparisons indicate the better performance of the proposed method.