Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system
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Researchers have suggested combining optimization-based control algorithms with ML techniques such as image recognition, regression trees, and time delay neural networks to reduce GHG emissions from HVAC systems.
“To reduce GHG emissions from HVAC systems, researchers have suggested combining optimization-based control algorithms with ML techniques such as image recognition, regression trees, and time delay neural networks [5,202] (see also 4.1).”
From Tackling climate change with machine learning (2023) · cites this paper as [5]
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Tackling climate change with machine learning
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- Authors
- Aftab, M., Chen, C., Chau, C.-K., & Rahwan, T.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Chau, Chi-Kin
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 154
- Pages
- 141-156
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2746463599
- Cited by
- 201
- References
- 54
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 29 · 2023: 38 · 2022: 24 · 2021: 29 · 2020: 21 · 2019: 17 · 2018: 17 · 2017: 3
- FWCI
- 14.83
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Energy and Buildings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- HVAC control, model predictive control, building automation, embedded systems, Raspberry Pi, EnergyPlus, building energy conservation
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