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Application of Fuzzy Time Series Approach in Electric Load Forecasting

Author

Listed:
  • Zuhaimy Ismail

    (Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Johor, Malaysia)

  • Riswan Efendi

    (Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Johor, Malaysia;
    Department of Mathematics, Faculty of Science and Technology, State Islamic University of Sultan Syarif Kasim Riau, 28294 Panam, Pekanbaru, Riau, Indonesia)

  • Mustafa Mat Deris

    (Faculty of Computer Science, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia)

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.

Suggested Citation

  • Zuhaimy Ismail & Riswan Efendi & Mustafa Mat Deris, 2015. "Application of Fuzzy Time Series Approach in Electric Load Forecasting," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 11(03), pages 229-248.
    • Handle: RePEc:wsi:nmncxx:v:11:y:2015:i:03:n:s1793005715500076
    DOI: 10.1142/S1793005715500076
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    Cited by:

    1. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    2. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    3. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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