Third-order fuzzy time series with trapezoidal membership for electricity consumption forecasting

There is an increment within the power charge of Universiti Tun Hussein Onn Malaysia (UTHM) lately. Thus, there is an ought to anticipate UTHM power utilization precisely to arrange for future vitality demand and utility saving decisions. Past researches on UTHM power utilization prediction have been carried out utilizing time series models, multiple linear regression, 1st-order fuzzy time series (FTS) and seasonal Autoregressive Integrated Moving Average (SARIMA). The 1st-order FTS yields the best accuracy among these four methods. Past forecasting studies showed higher order FTS can yield better accuracy. Thus, the 3rd -order FTS with trapezoidal membership function and 2009 to 2018 of monthly electricity consumption data were utilized to predict 2019 monthly electricity usage. The process of the 3rd -order FTS using trapezoidal membership function was shown in detail using January data. The process was repeated with other months. It was proved that 3rd -order FTS performs the best in overall data, but 2nd-order FTS predicts the future the best.

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