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Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

Author

Listed:
  • Zoran Gligorić

    (Faculty of Mining and Geology, Đušina 7, University of Belgrade, 11000 Belgrade, Serbia)

  • Svetlana Štrbac Savić

    (The School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, 11000 Belgrade, Serbia)

  • Aleksandra Grujić

    (The School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, 11000 Belgrade, Serbia)

  • Milanka Negovanović

    (Faculty of Mining and Geology, Đušina 7, University of Belgrade, 11000 Belgrade, Serbia)

  • Omer Musić

    (Faculty of Mining, Geology and Civil Engineering, Univerzitetska 2, 75000 Tuzla, Bosnia and Herzegovina)

Abstract

The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

Suggested Citation

  • Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1911-:d:159316
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    References listed on IDEAS

    as
    1. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
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    5. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    6. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    7. Marin Cerjan & Marin Matijaš & Marko Delimar, 2014. "Dynamic Hybrid Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 7(5), pages 1-15, May.
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    Cited by:

    1. Bikeri Adline & Kazushi Ikeda, 2023. "A Hawkes Model Approach to Modeling Price Spikes in the Japanese Electricity Market," Energies, MDPI, vol. 16(4), pages 1-20, February.

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