Cited by 883Smart Grid Energy ManagementBuilding Energy and Comfort OptimizationElectric Vehicles and Infrastructure

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Vázquez-Canteli, José R., Nagy, Zoltán·2019·Applied Energy

Integrity signals

Registry

Crossref · 10.1016/j.apenergy.2018.11.002

Cites

159 of 159 references verified0 citation errors · 0 not checkable

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  • Techniques such as deep RL, agent-based models, online optimization, and dynamic programming can help maximize profits for decentralized storage, demand, and generation, given real-time prices.

    “Techniques such as (deep) RL [291,292,810,881], agent-based models [173,662,663,863], online optimization [99], and dynamic programming [704] can then help maximize profits for decentralized storage, demand, and generation, given real-time prices.”

    From Tackling climate change with machine learning (2023) · cites this paper as [810]

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Authors
Vázquez-Canteli, J. R., & Nagy, Z.
Affiliations
The University of Texas at Austin (US)
Corresponding author
Nagy, Zoltán
Venue
Applied Energy
Publisher
Elsevier BV
Published
2019-02
Volume
235
Pages
1072-1089
ISSN
0306-2619 (ISSN-L)
OpenAlex
W2901645090
Cited by
883
References
159
Citations by year
2026: 155 · 2025: 144 · 2024: 122 · 2023: 111 · 2022: 115 · 2021: 111 · 2020: 95 · 2019: 29 · 2018: 1
FWCI
22.57
Type
journal article
Language
en
Open access
closed
Locations
  • Applied Energy (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
Keywords
demand response, demand-side management, reinforcement learning, smart grid, demand-side flexibility, peak demand shifting, multi-agent systems, HVAC control, electric vehicle charging, battery energy storage, renewable energy integration, building energy consumption

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