TY - GEN
T1 - A domain-Assisted data driven model for thermal comfort prediction in buildings
AU - Yang, Liang
AU - Zheng, Zimu
AU - Sun, Jingting
AU - Wang, Dan
AU - Li, Xin
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/6/12
Y1 - 2018/6/12
N2 - Recent studies on thermal comfort often require feedback from occupants or additional devices installed. This often limits the scalability of these approaches. In this paper, we for the first time study thermal comfort prediction of an occupant by training a model from the data of not only the targeted occupant but also others, guided by domain knowledge. We demonstrate, using ASHRAE data, that this approach has potential, and is worth exploring.
AB - Recent studies on thermal comfort often require feedback from occupants or additional devices installed. This often limits the scalability of these approaches. In this paper, we for the first time study thermal comfort prediction of an occupant by training a model from the data of not only the targeted occupant but also others, guided by domain knowledge. We demonstrate, using ASHRAE data, that this approach has potential, and is worth exploring.
KW - Applied machine learning
KW - Domain knowledge
KW - Thermal comfort
UR - http://www.scopus.com/inward/record.url?scp=85050193960&partnerID=8YFLogxK
U2 - 10.1145/3208903.3208914
DO - 10.1145/3208903.3208914
M3 - Conference contribution
AN - SCOPUS:85050193960
T3 - e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
SP - 271
EP - 276
BT - e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
PB - Association for Computing Machinery, Inc
T2 - 9th ACM International Conference on Future Energy Systems, e-Energy 2018
Y2 - 12 June 2018 through 15 June 2018
ER -