METHODS OF SHORT TERM ELECTRICITY DEMAND FORECASTING
Taganova G. Tussupov J. Abdildayeva A. Serikbayeva S. Sadirmekova Z.H. Azieva G. Mamatayeva D. Tolganbayeva M.
31 July 2022Little Lion Scientific
Journal of Theoretical and Applied Information Technology
2022#100Issue 145292 - 5299 pp.
This article discusses the short-term forecasting of electricity consumption. Methods of smoothing the daily schedule of power consumption are considered. The possibility of using one of the methods of smoothing power consumption was analyzed in Python. The proposed method is applicable for the subjects of REM in order to approximate the retrospective data of electricity consumption. The relevance of the work is due to the demand of the subjects of the wholesale electricity and capacity market (REM) for ways to build short-term forecasts of electricity consumption in order to improve the quality and accuracy of the predictive model. From the conducted research, it was revealed that the adaptive Holt-Winters smoothing method is optimal for making short-term forecasting for the day ahead.
Data Analysis, Python , Forecasting , Power Consumption
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L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan
Institute of Information and Computational Technologies CSMES RK, Almaty, Kazakhstan
Taraz Regional University named after M.KH. Dulaty, Taraz, Kazakhstan
L.N. Gumilyov Eurasian National University
Institute of Information and Computational Technologies CSMES RK
Taraz Regional University named after M.KH. Dulaty
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