Data-driven occupant modeling strategies and digital tools enabledby IEA EBC annex 79: poster abstract

Publication Year: 2018 Publication Type : ConferenceProceeding


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.


    author = {Mikkel Baun Kjærgaard and Bing Dong and Salvatore Carlucci and Flora D. Salim and Junjing Yang and Clinton J. Andrews and Omid Ardakanian},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Proceedings of the 5th Conference on Systems for Built Environments, BuildSys 2018, Shenzen, China, November 07-08, 2018},
    doi = {10.1145/3276774.3281015},
    editor = {Rajesh Gupta and Polly Huang and Marta Gonzalez},
    pages = {188--189},
    publisher = {ACM},
    timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
    title = {Data-driven occupant modeling strategies and digital tools enabled by IEA EBC annex 79: poster abstract},
    url = {},
    year = {2018}


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