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New Method of Modeling Daily Energy Consumption

Author

Listed:
  • Krzysztof Karpio

    (Institute of Information Technology, Warsaw University of Life-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Piotr Łukasiewicz

    (Institute of Information Technology, Warsaw University of Life-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Rafik Nafkha

    (Institute of Information Technology, Warsaw University of Life-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

Abstract

At present, papers concerning energy consumption and forecasting are predominantly dedicated to various known techniques and their combinations. On the other hand, the research on load modeling and forecasting methodologies is quite limited. This paper presents a new approach concerning hourly energy consumption using a multivariate linear regression model. The proposed technique provides a way to accurately model day-to-day energy consumption using just a few selected variables. The number of data points required to describe a whole day’s consumption depends on the demanded precision, which is up to the user. This model is self-configurable and very fast. The applied model shows that four hours are sufficient to describe energy consumption during the remainder of a given day. We show that for about 84% of the data points, the relative error of the model is below 2.5%, and for all the data points the error does not exceed 7.5%. We obtained a mean relative uncertainty of 1.72% in the learning data set, and 1.69% and 1.82% in the two testing data sets, respectively. In addition, we conclude that the model can also detect days with unusual energy consumption.

Suggested Citation

  • Krzysztof Karpio & Piotr Łukasiewicz & Rafik Nafkha, 2023. "New Method of Modeling Daily Energy Consumption," Energies, MDPI, vol. 16(5), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2095-:d:1075553
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    References listed on IDEAS

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    Cited by:

    1. Krzysztof Karpio & Piotr Łukasiewicz & Tomasz Ząbkowski, 2024. "Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System," Energies, MDPI, vol. 17(11), pages 1-14, May.
    2. Tomasz Ząbkowski & Krzysztof Gajowniczek & Grzegorz Matejko & Jacek Brożyna & Grzegorz Mentel & Małgorzata Charytanowicz & Jolanta Jarnicka & Anna Olwert & Weronika Radziszewska, 2023. "Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland," Energies, MDPI, vol. 16(19), pages 1-17, September.

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