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Energy Consumption Forecasting for the Digital-Twin Model of the Building

Author

Listed:
  • Joanna Henzel

    (Department of Computer Networks and Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland)

  • Łukasz Wróbel

    (Department of Computer Networks and Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland)

  • Marcin Fice

    (Prosumer Energy Center, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland)

  • Marek Sikora

    (Department of Computer Networks and Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland)

Abstract

The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building.

Suggested Citation

  • Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4318-:d:837638
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    References listed on IDEAS

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    Cited by:

    1. Lv, Zhihan & Cheng, Chen & Lv, Haibin, 2023. "Digital twins for secure thermal energy storage in building," Applied Energy, Elsevier, vol. 338(C).
    2. Imre Kovách & Boldizsár Gergely Megyesi, 2023. "Energy Use Research in the Social Sciences–Introduction to a Research Topic," Energies, MDPI, vol. 16(8), pages 1-8, April.
    3. Bâra, Adela & Oprea, Simona-Vasilica, 2024. "Enabling coordination in energy communities: A Digital Twin model," Energy Policy, Elsevier, vol. 184(C).
    4. Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).
    5. Marian Kampik & Marcin Fice & Adam Pilśniak & Krzysztof Bodzek & Anna Piaskowy, 2023. "An Analysis of Energy Consumption in Small- and Medium-Sized Buildings," Energies, MDPI, vol. 16(3), pages 1-21, February.

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