Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States
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DOI: 10.1016/j.rser.2019.109593
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Cited by:
- Skiba, Marta & Mrówczyńska, Maria & Sztubecka, Małgorzata & Bazan-Krzywoszańska, Anna & Kazak, Jan K. & Leśniak, Agnieszka & Janowiec, Filip, 2021. "Probability estimation of the city’s energy efficiency improvement as a result of using the phase change materials in heating networks," Energy, Elsevier, vol. 228(C).
- Samy Faddel & Guanyu Tian & Qun Zhou, 2021. "Decentralized Management of Commercial HVAC Systems," Energies, MDPI, vol. 14(11), pages 1-18, May.
- Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
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Keywords
Thermal comfort; Bayesian inference; ASHRAE thermal comfort database; Active learning; Temperature set-point; Office buildings;All these keywords.
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