Forecasting electricity consumption time series in the R programming environment

To ensure effective management of energy systems, it is necessary to analyze and forecast time series of electricity consumption. Obtaining accurate forecasts of electricity consumption to optimize the operation of energy networks, planning the production and distribution of electricity explains the relevance of this study. This article presents a comparative analysis of medium-term electricity consumption forecasting models utilizing the R programming environment. The study encompasses classical forecasting models such as SARIMA and ETS, as well as less commonly referenced machine-oriented models like TBATS and Prophet in the scientific literature. The paper details the R functions necessary for performing calculations and includes a code snippet intended for preliminary data analysis and forecasting. All examined models demonstrate high accuracy in medium-term electricity consumption forecasting. However, variability in model fitting quality metrics is observed depending on the regional branches of the Unified Energy System of Russia. The application of ETS algorithms and bagging ETS yields the best forecasts with a minimal mean absolute error (slightly over 1%) for Russia as a whole, as well as for the consolidated energy system of the Urals. The TBATS model is recommended for predicting electricity consumption in the Center and East zones, while SARIMA is suggested for the South zone. Although the Prophet model exhibited satisfactory forecasting quality, the analysis indicates that its effectiveness significantly increases when applied to high-frequency data, such as weekly or hourly time series.