Non-intrusive Indoor Occupancy Detection Methods Based on Machine Learning Techniques

Weilin He, Yuanyuan Ma (), Cheng Fan, Xinghua Wang and Bufu Huang
Additional contact information
Weilin He: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Yuanyuan Ma: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Cheng Fan: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Xinghua Wang: eSight Technology (Shenzhen) Company Limited
Bufu Huang: eSight Technology (Shenzhen) Company Limited

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 1186-1201 from Springer

Abstract: Abstract The Heating, Ventilation and Air-Conditioning (HVAC) systems in public buildings typically operate on fixed schedules without considering changes in actual indoor occupancy. Significant energy savings can be achieved by adaptively adjusting HVAC operating conditions to meet indoor cooling demands. Obtaining indoor occupancy through conventional devices, such as video cameras and personal bracelets, may impose negative impacts on human privacy. To address such challenge, this study proposes a non-intrusive method to accurately predict indoor occupancy using environmental data and machine learning techniques. Two-month experiment has been designed to obtain high-frequency data on environmental conditions and indoor occupancy of a conference room. A variety of environmental data, including temperature, relative humidity, CO2 concentration, light intensity and noise level, have been collected and used as modeling inputs. Four state-of-the-art machine learning techniques, together with over-sampling and under-sampling techniques, have been used for indoor occupancy detection and occupant number prediction. The model performance has been carefully analyzed to investigate the potential of the non-intrusive method proposed. The research results validate the usefulness of environmental data in predicting indoor occupancy. The research outcomes are helpful for devising occupancy-centric measures for building energy conservations.

Keywords: Indoor occupancy; Non-intrusive detection; Machine learning; Data sampling; Building energy conservations (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-19-5256-2_93

Ordering information: This item can be ordered from
http://www.springer.com/9789811952562

DOI: 10.1007/978-981-19-5256-2_93

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().