Abstract
In electrical power management, load forecasting accuracy is an indispensable factor which influences the decision making and planning of power companies in the future. Previous research has explored various forecasting models to resolve this issue, ranging from linear and non-linear regression to artificial intelligence algorithm. However, the absolute percentage error has yet to significantly improve using these models. Through this paper, the fuzzy time series (FTS) model was suggested to obtain better forecasted values and increases the forecasting accuracy. This accuracy could be obtained by using effective length of intervals of the discourse universe. The yearly dataset of Taiwan regional electric load was used for this empirical study and the reliability of the proposed model was compared with other previous models. The results indicated that the mean absolute percentage error (MAPE) of the proposed model (FTS) is smaller than MAPE obtained from those previous models.
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