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Occupancy estimation with environmental sensors: The possibilities and limitations.

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Bibliographic Details
Title:	Occupancy estimation with environmental sensors: The possibilities and limitations.
Authors:	Chitnis, Shubham, Somu, Nivethitha, Kowli, Anupama
Source:	Energy & Built Environment; Feb2025, Vol. 6 Issue 1, p96-108, 13p
Subject Terms:	MACHINE learning, INTELLIGENT buildings, SAMPLING (Process), PROBLEM solving, WELL-being
Abstract:	Occupancy detection and estimation in buildings paves the way to improve the utilization of lighting and HVAC systems, induce energy savings and enhance the well-being of the occupants. This paper presents a comparative study of state-of-art machine learning techniques that solve two different occupancy monitoring problems using environmental sensor data. One is the regression problem that estimates the actual count of occupants while the other is the classification problem which estimates the level of occupancy (empty, sparse, full). The results of the best performing machine learning techniques that solve both problems for the open dataset from the University of Southern Denmark, Odense are presented to compare the accuracy of both approaches and the ease of implementation. The impact of CO2, temperature, and humidity features on the occupancy count/levels and detection accuracy (occupied versus unoccupied) are studied. Comprehensive analysis with different combinations of environmental features and other free features such as time-of-day along with different sampling techniques for training and testing are performed to understand how such models can be adapted for actual deployment. Our results indicate detection accuracy between 66% to 82% for different sampling schemes; with day-based sampling showing a better performance while random sampling generically showcasing lower accuracy (66.2%). The occupancy estimation (levels or counts) has accuracy in the range of 69% to 79% for random sampling and 71% to 80% for day-based sampling. Finally, results demonstrate that models based single environmental sensor data streams do not perform as well as the models with sensor fusion. [ABSTRACT FROM AUTHOR]
	Copyright of Energy & Built Environment is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Complementary Index

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