Deep Learning and Reinforcement Learning for Modeling Occupants’ Information in an Occupant-Centric Building Control: A Systematic Literature Review-Reference-Cited by-同舟云学术

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Deep Learning and Reinforcement Learning for Modeling Occupants’ Information in an Occupant-Centric Building Control: A Systematic Literature Review

Published:2024-03-18 Issue: Volume:2 Page:186-195
ISSN:
Container-title:Construction Research Congress 2024
language:
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Author:

Adhikari Rosina¹²³⁴,Gautam Yogesh¹²³⁴,Jebelli Houtan¹²³⁴,Sitzabee Willian E.¹²³⁴

Affiliation:

1. Ph.D. Student, Dept. of Architectural Engineering, Pennsylvania State Univ., University Park, State College, PA.

2. Ph.D. Student, Dept. of Civil and Environmental Engineering, Univ. of Illinois Urbana-Campaign, Champaign, IL.

3. Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Illinois Urbana-Campaign, Champaign, IL.

4. Vice President, Chief Facilities Officer, and Professor of Architectural Engineering, Pennsylvania State Univ., University Park, PA.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485262.020

Reference40 articles.

1. Reinforcement learning for whole-building HVAC control and demand response;Azuatalam D.;Energy and AI,2020

2. Bayer D. and M. Pruckner. 2022. “Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems.” 2022 IEEE Conference on Technologies for Sustainability SusTech 2022 187–194.

3. Deep vision-based occupancy counting: Experimental performance evaluation and implementation of ventilation control;Choi H.;Build Environ,2022

4. Vision-based estimation of clothing insulation for building control: A case study of residential buildings;Choi H.;Build Environ,2021

5. Application of vision-based occupancy counting method using deep learning and performance analysis;Choi H.;Energy Build,2021

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