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

Authors:

Mikkel Baun Kjærgaard,

Omid ArdakanianAuthors Info & Claims

BuildSys '18: Proceedings of the 5th Conference on Systems for Built Environments

Pages 188 - 189

https://doi.org/10.1145/3276774.3281015

Published: 07 November 2018 Publication History

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Abstract

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.

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Carlucci SDe Simone MFirth SKjærgaard MMarkovic RRahaman MAnnaqeeb MBiandrate SDas ADziedzic JFajilla GFavero MFerrando MHahn JHan MPeng YSalim FSchlüter Avan Treeck C(2020)Modeling occupant behavior in buildingsBuilding and Environment10.1016/j.buildenv.2020.106768(106768)Online publication date: Feb-2020
https://doi.org/10.1016/j.buildenv.2020.106768
Park JChen JJin XNagy Z(2019)Investigating occupancy profiles using convolutional neural networksProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360989(338-339)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3360322.3360989

Index Terms

Data-driven occupant modeling strategies and digital tools enabled by IEA EBC annex 79: poster abstract
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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BuildSys '18: Proceedings of the 5th Conference on Systems for Built Environments

November 2018

211 pages

ISBN:9781450359511

DOI:10.1145/3276774

General Chair:
Rajesh Gupta
University of Califronia, San Diego
,
Program Chairs:
Polly Huang
National Taiwan University and Keio University
,
Marta Gonzalez
University of California, Berkeley

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 07 November 2018

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BuildSys '18: The 5th ACM International Conference on Systems for Built Environments

November 7 - 8, 2018

Shenzen, China

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Carlucci SDe Simone MFirth SKjærgaard MMarkovic RRahaman MAnnaqeeb MBiandrate SDas ADziedzic JFajilla GFavero MFerrando MHahn JHan MPeng YSalim FSchlüter Avan Treeck C(2020)Modeling occupant behavior in buildingsBuilding and Environment10.1016/j.buildenv.2020.106768(106768)Online publication date: Feb-2020
https://doi.org/10.1016/j.buildenv.2020.106768
Park JChen JJin XNagy Z(2019)Investigating occupancy profiles using convolutional neural networksProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360989(338-339)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3360322.3360989

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Room-level occupant counts, airflow and CO₂ data from an office building

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Room-level occupant counts, airflow and CO2 data from an office building

Understanding building occupant activities at scale: An integrated knowledge-based and data-driven approach

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Room-level occupant counts, airflow and CO₂ data from an office building