A domain-Assisted data driven model for thermal comfort prediction in buildings

Liang Yang, Zimu Zheng, Jingting Sun, Dan Wang, Xin Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicatione-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
PublisherAssociation for Computing Machinery, Inc
Pages271-276
Number of pages6
ISBN (Electronic)9781450357678
DOIs
Publication statusPublished - 12 Jun 2018
Event9th ACM International Conference on Future Energy Systems, e-Energy 2018 - Karlsruhe, Germany
Duration: 12 Jun 201815 Jun 2018

Publication series

Namee-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems

Conference

Conference9th ACM International Conference on Future Energy Systems, e-Energy 2018
Country/TerritoryGermany
CityKarlsruhe
Period12/06/1815/06/18

Keywords

  • Applied machine learning
  • Domain knowledge
  • Thermal comfort

ASJC Scopus subject areas

  • Fuel Technology
  • Energy Engineering and Power Technology

Cite this