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

Published: 04 November 2018 Publication History

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

[1]
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.
[2]
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.
[3]
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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Bulletin of China Green University network. 2017. China Green University network. Retrieved June 23, 2018 from https://china.lbl.gov/sites/default/files/misc/zhong_guo_lu_se_da_xue_lian_meng_jie_shao_-zhong_ying_wen_dui_zhao_201710.pdf
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Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018).
[6]
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.
[7]
C. Zhang and Q. S. Jia. 2017. An occupancy distribution estimation method using the surveillance cameras in buildings. In 2017 13th IEEE Conference on Automation Science and Engineering (CASE). 894--899.

Cited By

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  • (2024)A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space TypesIEEE Access10.1109/ACCESS.2024.338391812(50202-50216)Online publication date: 2024
  • (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

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

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    cover image ACM Conferences
    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
    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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    New York, NY, United States

    Publication History

    Published: 04 November 2018

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    Author Tags

    1. Energy Management
    2. HVAC Control
    3. Occupancy-driven

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    View all
    • (2024)A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space TypesIEEE Access10.1109/ACCESS.2024.338391812(50202-50216)Online publication date: 2024
    • (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

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