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iOccupancy: An Investigation of Online Occupancy-driven HVAC Control in Campus Classrooms

Authors:

Chunhua Chen,

Yubin Ruan, and

Zhongwen LiaoAuthors Info & Claims

CitiFog'18: Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing

November 2018

Pages 25 - 28

https://doi.org/10.1145/3277893.3277900

Published: 04 November 2018 Publication History

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Abstract

Accurate and timely occupancy data is critical for occupancy-driven energy efficiency in smart building management. This paper presents an on-going project iOccupancy which elaborates the practice of employing a CNN-based object detection algorithm YOLOv3 (version 3) for occupancy counting using images from surveillance cameras and online occupancy-driven HVAC control in campus classrooms. Our experiments show that counting occupants with the YOLOv3 object detector can only achieve an accuracy around 60% in large classrooms with a single camera located at the back of classrooms, indicating that more than one cameras might be needed for practical usage. Energy saving opportunities are also justified in real use cases of classrooms, comparing to fixed HVAC scheduling (e.g. using course time tables).

References

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Yuvraj Agarwal, Bharathan Balaji, Rajesh Gupta, Jacob Lyles, Michael Wei, and Thomas Weng. 2010. Occupancy-driven Energy Management for Smart Building Automation. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys '10). ACM, New York, NY, USA, 1--6.

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Francesco Conti, Antonio Pullini, and Luca Benini. 2014. Brain-inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.

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Varick L. Erickson, Yiqing Lin, Ankur Kamthe, Rohini Brahme, Amit Surana, Alberto E. Cerpa, Michael D. Sohn, and Satish Narayanan. 2009. Energy Efficient Building Environment Control Strategies Using Real-time Occupancy Measurements. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys '09). ACM, New York, NY, USA, 19--24.

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Amee Trivedi, Jeremy Gummeson, David Irwin, Deepak Ganesan, and Prashant Shenoy. 2017. iSchedule: Campus-scale HVAC Scheduling via Mobile WiFi Monitoring. In Proceedings of the Eighth International Conference on Future Energy Systems (e-Energy '17). ACM, New York, NY, USA, 132--142.

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Rafiq IMahmood AAhmed UKhan AArshad KAssaleh KRatyal NZoha A(2024)A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space TypesIEEE Access10.1109/ACCESS.2024.338391812(50202-50216)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383918
Choi HUm CKang KKim HKim T(2021)Application of vision-based occupancy counting method using deep learning and performance analysisEnergy and Buildings10.1016/j.enbuild.2021.111389252(111389)Online publication date: Dec-2021
https://doi.org/10.1016/j.enbuild.2021.111389

Index Terms

iOccupancy: An Investigation of Online Occupancy-driven HVAC Control in Campus Classrooms
1. Information systems
  1. Information systems applications

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Published In

CitiFog'18: Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing

November 2018

47 pages

ISBN:9781450360517

DOI:10.1145/3277893

Editors:
Gowri Sankar Ramachandran
University of Southern California, Los Angeles, USA
,
Bhaskar Krishnamachari
University of Southern California, Los Angeles, USA
,
Program Chairs:
Sid Chi-Kin Chau
Australian National University, Australia
,
Yang Yang
Shanghai Institute of Fog Computing Technology, ShanghaiTech University, China
,
Tung Fung
Chinese University of Hong Kong, Hong Kong

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 November 2018

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SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems

November 4, 2018

Shenzhen, China

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Rafiq IMahmood AAhmed UKhan AArshad KAssaleh KRatyal NZoha A(2024)A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space TypesIEEE Access10.1109/ACCESS.2024.338391812(50202-50216)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383918
Choi HUm CKang KKim HKim T(2021)Application of vision-based occupancy counting method using deep learning and performance analysisEnergy and Buildings10.1016/j.enbuild.2021.111389252(111389)Online publication date: Dec-2021
https://doi.org/10.1016/j.enbuild.2021.111389

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Multi-agent system for energy consumption optimisation in higher education institutions

ThermoSense: Occupancy Thermal Based Sensing for HVAC Control

Inferring Smart Schedules for Dumb Thermostats

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Multi-agent system for energy consumption optimisation in higher education institutions

ThermoSense: Occupancy Thermal Based Sensing for HVAC Control

Inferring Smart Schedules for Dumb Thermostats

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