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Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning

Bibliographic Data

ID21234865
AuthorsSafoura Faghri (University of Delaware), Hamed Tahami (0009-0007-3210-1921, Politecnico di Milano), Reza Amini (0000-0002-0634-1469, Texas Water Development Board), Haniyeh Katiraee (Islamic Azad University Roudehen Branch), Amir Saman Godazi Langeroudi (0000-0002-4031-9214), Mahyar Alinejad (University of Central Florida), Mobin Ghasempour Nejati (0000-0001-9414-618X, University of California, Irvine, corresponding author)
Year2025
Volume119
Pages106077
Publication date2025-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSustainable Cities and Society (JOURNAL)
Journal identifiersISSN: 2210-6707 • E-ISSN: 2210-6715
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2024.106077
OpenAlexW4405414068
LanguageEN
Citations received4
References cited44

Architectural engineering · Distributed computing · Electrical engineering · Geography · Grid · Reinforcement · Reinforcement learning · Smart grid · Structural engineering · Computer Science · Engineering · Mathematics · Microgrid Control and Optimization · Optimal Power Flow Distribution · Smart Grid Energy Management · Artificial Intelligence

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    Open Access•Reza Sepehrzad, Amir Saman Godazi Langeroudi et al.•Sustainable Cities and Society•2024

  • An overview of machine learning applications for smart buildings

    Open Access•Kari Alanne, Seppo Sierla•Sustainable Cities and Society•2022

  • Sustaining power distribution network submerged in plug-in hybrid CNG-electric vehicles for future green transportation

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Unique citing works4
Citations per year4
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 4

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