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A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

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  • Aguilar, J.
  • Garces-Jimenez, A.
  • R-Moreno, M.D.
  • García, Rodrigo

Abstract

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of “Autonomous Cycles of Data Analysis Tasks”, which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.

Suggested Citation

  • Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:rensus:v:151:y:2021:i:c:s136403212100808x
    DOI: 10.1016/j.rser.2021.111530
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    References listed on IDEAS

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    Cited by:

    1. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Imma Bortone & Hacer Sakar & Ana Soares, 2022. "Gaps in Regulation and Policies on the Application of Green Technologies at Household Level in the United Kingdom," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    3. Meera P. S. & Lavanya V., 2023. "Nature-Inspired Algorithms for Energy Management Systems: A Review," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-16, January.
    4. Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
    5. Fan, Cheng & Chen, Ruikun & Mo, Jinhan & Liao, Longhui, 2024. "Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures," Applied Energy, Elsevier, vol. 362(C).

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