Energy-efficient heating control for nearly zero energy residential buildings with deep reinforcement learning
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DOI: 10.1016/j.energy.2022.126209
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- Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
- Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
- Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
- Jan Wrana & Wojciech Struzik & Katarzyna Jaromin-Gleń & Piotr Gleń, 2023. "FCH HVAC Honeycomb Ring Network—Transition from Traditional Power Supply Systems in Existing and Revitalized Areas," Energies, MDPI, vol. 16(24), pages 1-14, December.
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Keywords
HVAC; Optimal control; Reinforcement learning; Deep Q learning; Prioritized replay; Model-free control;All these keywords.
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