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This paper presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23 % through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan. [ABSTRACT FROM AUTHOR]

In this article, an approach based on neural network combined with a fuzzy system is proposed for three-hours-ahead load forecasting. In this proposed prediction method, the forecasted load is obtained by adding a correction that is obtained from the neural network and a fuzzy logic to the selected similar days data. The neural network has the advantage of dealing with the nonlinear part of forecasted load curves and the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these methods, the proposed method is useful in situations where accurate forecasting models are difficult to obtain. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan. [ABSTRACT FROM AUTHOR]