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Open AccessArticle

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

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Faculty of Mining and Geology, Đušina 7, University of Belgrade, 11000 Belgrade, Serbia
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The School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, 11000 Belgrade, Serbia
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Faculty of Mining, Geology and Civil Engineering, Univerzitetska 2, 75000 Tuzla, Bosnia and Herzegovina
*
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1911; https://doi.org/10.3390/en11071911
Received: 24 June 2018 / Revised: 7 July 2018 / Accepted: 9 July 2018 / Published: 22 July 2018
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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. View Full-Text
Keywords: electricity price series; fuzzy C-mean; interval series; interval autoregressive forecasting electricity price series; fuzzy C-mean; interval series; interval autoregressive forecasting
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Gligorić, Z.; Savić, S.Š.; Grujić, A.; Negovanović, M.; Musić, O. Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process. Energies 2018, 11, 1911.

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