Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods

Paweł Pełka ()
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Paweł Pełka: Electrical Engineering Faculty, Czestochowa University of Technology, 42-200 Czestochowa, Poland

Energies, 2023, vol. 16, issue 2, 1-22

Abstract: This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal fluctuation cycles, long-term trends, instability, and random noise. In order to simplify the prediction issue, the monthly load time series is represented by an annual cycle pattern, which unifies the data and filters the trends. A simulation study performed on the monthly electricity load time series for 35 European countries confirmed the high accuracy of the proposed models.

Keywords: medium-term load forecasting; pattern-based forecasting; time-series preprocessing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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