Modeling techniques used in building HVAC control systems: A review
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ML approaches such as deep belief networks can potentially increase temperature forecasting accuracy with less computational expense.
“however, ML approaches such as deep belief networks can potentially increase accuracy with less computational expense [4,255] (see also Section 5.3).”
From Tackling climate change with machine learning (2023) · cites this paper as [4]
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All you need to know about model predictive control for buildings
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- Authors
- Afroz, Z., Shafiullah, G., Urmee, T., & Higgins, G.
- Affiliations
- Murdoch University (AU)
- Corresponding author
- Afroz, Zakia
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 83
- Pages
- 64-84
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2773069998
- Cited by
- 411
- References
- 200
- Citations by year
- 2026: 30 · 2025: 57 · 2024: 67 · 2023: 61 · 2022: 63 · 2021: 48 · 2020: 29 · 2019: 42 · 2018: 14
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- Murdoch University Research Portal (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- Murdoch University Research Scholarship (MURS)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Mechanical Engineering › Refrigeration and Air Conditioning Technologies
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- HVAC control, building energy simulation, building energy efficiency, advanced control strategies, indoor thermal comfort, indoor air quality, model limitations, unmeasured disturbances
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