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Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions

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  • Panagiotis Michailidis

    (Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
    Electrical and Computer Engineering, Democritus University of Thrace, 67132 Xanthi, Greece
    These authors contributed equally to this work.)

  • Iakovos Michailidis

    (Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
    These authors contributed equally to this work.)

  • Socratis Gkelios

    (Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
    Electrical and Computer Engineering, Democritus University of Thrace, 67132 Xanthi, Greece
    These authors contributed equally to this work.)

  • Elias Kosmatopoulos

    (Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
    Electrical and Computer Engineering, Democritus University of Thrace, 67132 Xanthi, Greece
    These authors contributed equally to this work.)

Abstract

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted.

Suggested Citation

  • Panagiotis Michailidis & Iakovos Michailidis & Socratis Gkelios & Elias Kosmatopoulos, 2024. "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, MDPI, vol. 17(3), pages 1-47, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:570-:d:1325697
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    References listed on IDEAS

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