Gigawatt-hour scale savings on a budget of zero: Deep reinforcement learning based optimal control of hot water systems
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Kazmi et al. used deep RL to achieve a scalable 20% reduction of energy while requiring only three sensors.
“For control, Kazmi et al. [417] used deep RL to achieve a scalable 20% reduction of energy while requiring only three sensors: air temperature, water temperature, and energy use (see also Section 5.3 for similarly substantial gains in datacenter cooling).”
From Tackling climate change with machine learning (2023) · cites this paper as [417]
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- Authors
- Kazmi, H., Mehmood, F., Lodeweyckx, S., & Driesen, J.
- Affiliations
- KU Leuven (BE)
- Lahore University of Management Sciences (PK)
- Corresponding author
- Kazmi, Hussain
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 144
- Pages
- 159-168
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2774636080
- Cited by
- 109
- References
- 41
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 13 · 2023: 14 · 2022: 14 · 2021: 19 · 2020: 14 · 2019: 14 · 2018: 4
- FWCI
- 7.70
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Energy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agentschap Innoveren en Ondernemen
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- domestic hot water heating, deep reinforcement learning, optimal control, energy consumption reduction, occupant comfort, model-free learning, hot water storage tank, residential energy efficiency, building energy efficiency
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