Data-driven occupant modeling strategies and digital tools enabled by IEA EBC annex 79

Mikkel Baun Kjærgaard, Bing Dong, Salvatore Carlucci, Flora Salim, Junjing Yang, Clinton Andrews, Omid Ardakanian

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsResearchpeer-review


The developments in sensing modalities and computing platforms enable many new sensing technologies and data sources for monitoring occupant presence and actions. The wealth of data opens new opportunities for extracting knowledge through data-driven modeling of occupant presence and actions. In particular, the many opportunities with machine learning techniques including supervised and unsupervised learning for classification, regression and clustering problems. Utilizing these opportunities creates new models and information relevant for generating new knowledge on multi-aspect environmental exposure, building interfaces, human behaviour, occupant-centric building design and operation. Subtask 2 of the new IEA EBC Annex 79 is addressing these opportunities and is inviting researchers and practitioners to participate. The developed data-driven models can, among others, be applied for example for calculating new schedules or models based on the actual conditions observed in buildings, data-driven analysis of the performance design versus the built, model predictive controls for buildings and fault detection and diagnostics.

Original languageEnglish
Title of host publicationProceedings of the 5th Conference on Systems for Built Environments
EditorsGowri Sankar Ramachandran, Nipun Batra
PublisherAssociation for Computing Machinery
Publication date7. Nov 2018
ISBN (Electronic)978-1-4503-5951-1
Publication statusPublished - 7. Nov 2018
Event5th Conference on Systems for Built Environments - Shenzen, China
Duration: 7. Nov 20188. Nov 2018
Conference number: 5


Conference5th Conference on Systems for Built Environments


  • Data-driven
  • Occupant Modeling
  • Occupant Sensing


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