Abstract
This paper introduces a new hybrid neuro-fuzzy model, called HNFB, and evaluates its performance in short-term load forecasting. To this end, two Brazilian electric power companies were used as case studies. A total of three intelligent forecasting systems were tested and compared: neural networks, neuro-fuzzy, and neural/neuro-fuzzy systems. As input variables, the experiments made use of historical load series and of additional variables that influence the load behavior, such as the temperature, the comfort index and the consumption profile. The results reveal the potential of the proposed neuro-fuzzy and neural/neuro-fuzzy models for load forecasting, when compared with neural networks; the mean absolute percentage errors varied between 0.44 and 1.95%, depending on the case study at hand.
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