Machine Learning for Smart and Energy-Efficient Buildings
Open accessAbstract
Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Machine learning (ML) has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review some of the most promising ways in which ML has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction to the relevant ML paradigms and the components and functioning of each smart building system we cover. Finally, we discuss the challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research in this field.
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Domain-specific semantics in energy use cases create additional barriers for explainability.
“Domain-specific semantics in energy use cases, such as mixed energy carrier systems or HVAC comfort-cost trade-offs [64], create additional barriers.”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [64]
Cites
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Self-citationReinforcement learning, fast and slow
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Approximate model predictive building control via machine learning
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Álvarez-López, C., González-Briones, A., & Li, T. (2026) · Electronics · doi:10.3390/electronics15020385 · Cited by 23 · see citing works
- Authors
- Das, H. P., Lin, Y.-W., Agwan, U., Spangher, L., Devonport, A., Yang, Y., Drgoňa, J., Chong, A., Schiavon, S., & Spanos, C. J.
- Affiliations
- University of California, Berkeley (US)
- Xi'an Jiaotong University (CN)
- Pacific Northwest National Laboratory (US)
- National University of Singapore (SG)
- Corresponding author
- Das, Hari Prasanna
- ORCID
- Venue
- Environmental Data Science
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024
- Volume
- 3
- ISSN
- 2634-4602 (ISSN-L)
- OpenAlex
- W4390583408
- Cited by
- 78
- References
- 5
- Citations by year
- 2026: 24 · 2025: 38 · 2024: 15 · 2023: 1
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Environmental Data Science (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- smart buildings, building energy efficiency, building energy consumption, occupant comfort, building automation, energy optimization
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