More Web Proxy on the site http://driver.im/

research-article

Non-Intrusive Techniques for Establishing Occupancy Related Energy Savings in Commercial Buildings

Authors:

Omid Ardakanian,

Arka Bhattacharya,

David CullerAuthors Info & Claims

BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

Pages 21 - 30

https://doi.org/10.1145/2993422.2993574

Published: 16 November 2016 Publication History

Abstract

The design of energy-efficient commercial building Heating Ventilation and Air Conditioning (HVAC) systems has been in the forefront of energy conservation efforts over the past few decades. The HVAC systems traditionally run on a static schedule that does not take occupancy into account, wasting a lot of energy in conditioning empty or partially-occupied spaces. This paper investigates the application of non-intrusive techniques to obtain a rough estimate of occupancy from coarse-grained measurements of the sensors that are commonly available through the building management system. Various per-zone schedules can be developed based on this approximate knowledge of occupancy at the level of individual zones. Our experiments in three large commercial buildings confirm that the proposed techniques can uncover the occupancy pattern of the zones, and schedules that incorporate these occupancy patterns can achieve more than 38% reduction in reheat energy consumption while maintaining indoor thermal comfort.

Supplementary Material

MOV File (p21.mov)

Download
554.09 MB

References

[1]

Y. Agarwal, B. Balaji, S. Dutta, R. K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next frontier in HVAC control. In IPSN, pages 246--257, April 2011.

[2]

ASHRAE. Standard 90.1-2013. https://www.ashrae.org/resources--publications/bookstore/standard-90-1.

[3]

A. Aswani, N. Master, J. Taneja, V. Smith, A. Krioukov, D. Culler, and C. Tomlin. Identifying models of HVAC systems using semiparametric regression. In American Control Conference (ACC), pages 3675--3680, June 2012.

[4]

B. Balaji, J. Xu, A. Nwokafor, R. Gupta, and Y. Agarwal. Sentinel: Occupancy based HVAC actuation using existing wifi infrastructure within commercial buildings. In SenSys, pages 17:1--17:14. ACM, 2013.

Digital Library

[5]

A. Beltran, V. L. Erickson, and A. E. Cerpa. Thermosense: Occupancy thermal based sensing for HVAC control. In BuildSys, pages 11:1--11:8. ACM, 2013.

Digital Library

[6]

J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679--698, June 1986.

Digital Library

[7]

D. Chen, S. Barker, A. Subbaswamy, D. Irwin, and P. Shenoy. Non-intrusive occupancy monitoring using smart meters. In BuildSys, pages 9:1--9:8. ACM, 2013.

Digital Library

[8]

D. B. Crawley, C. O. Pedersen, L. K. Lawrie, and F. C. Winkelmann. EnergyPlus: Energy simulation program. ASHRAE Journal, 42:49--56, 2000.

[9]

CUErgo. Ambient environment: Thermal conditions. http://ergo.human.cornell.edu/studentdownloads/DEA3500notes/Thermal/thcondnotes.html, 2016, retrieved.

[10]

B. Dong and K. P. Lam. Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network. Building Performance Simulation, 4(4):359--369, 2011.

[11]

A. Ebadat, G. Bottegal, D. Varagnolo, B. Wahlberg, and K. H. Johansson. Regularized deconvolution-based approaches for estimating room occupancies. IEEE Transactions on Automation Science and Engineering, 12(4):1157--1168, Oct 2015.

[12]

V. L. Erickson, S. Achleitner, and A. E. Cerpa. POEM: Power-efficient occupancy-based energy management system. In IPSN, pages 203--216. ACM, 2013.

Digital Library

[13]

R. Fontugne, J. Ortiz, N. Tremblay, P. Borgnat, P. Flandrin, K. Fukuda, D. Culler, and H. Esaki. Strip, bind, and search: A method for identifying abnormal energy consumption in buildings. In IPSN, pages 129--140. ACM, 2013.

Digital Library

[14]

S. K. Ghai, L. V. Thanayankizil, D. P. Seetharam, and D. Chakraborty. Occupancy detection in commercial buildings using opportunistic context sources. In Pervasive Computing and Communications Workshops, IEEE International Conference on, pages 463--466, March 2012.

[15]

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 454(1971):903--995, 1998.

[16]

M. Jin, N. Bekiaris-Liberis, K. Weekly, C. Spanos, and A. Bayen. Sensing by proxy: Occupancy detection based on indoor CO2 concentration. In Proceedings of the 9th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pages 1--10, 2015.

[17]

A. Kamthe, V. Erickson, M. A. Carreira-Perpiñán, and A. Cerpa. Enabling building energy auditing using adapted occupancy models. In BuildSys, pages 31--36. ACM, 2011.

Digital Library

[18]

W. Kleiminger, C. Beckel, and S. Santini. Household occupancy monitoring using electricity meters. In UbiComp, pages 975--986. ACM, 2015.

Digital Library

[19]

V. B. Krishna, D. Jung, N. Q. M. Khiem, H. H. Nguyen, and D. K. Y. Yau. Energytrack: Sensor-driven energy use analysis system. In BuildSys, pages 38:1--38:2. ACM, 2013.

Digital Library

[20]

J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. The smart thermostat: Using occupancy sensors to save energy in homes. In SenSys, pages 211--224. ACM, 2010.

Digital Library

[21]

K. Padmanabh, A. Malikarjuna, V, S. Sen, S. P. Katru, A. Kumar, S. P. C, S. K. Vuppala, and S. Paul. isense: A wireless sensor network based conference room management system. In BuildSys, pages 37--42. ACM, 2009.

Digital Library

[22]

K. W. Roth, D. Westphalen, P. Llana, and M. Feng. The energy impact of faults in U.S. commercial buildings. In Int'l Refrigeration and Air Conditioning Conference, 2004.

[23]

J. Taneja, A. Krioukov, S. Dawson-Haggerty, and D. Culler. Enabling advanced environmental conditioning with a building application stack. In International Green Computing Conference (IGCC), pages 1--10. IEEE, 2013.

[24]

U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. Buildings energy data book, 2011.

Cited By

Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Kolvig-Raun EKjærgaard MBrorsen R(2023)EDDE: An Event-Driven Data Exchange to Accurately Introspect Cobot Applications2023 IEEE/ACM 5th International Workshop on Robotics Software Engineering (RoSE)10.1109/RoSE59155.2023.00009(25-30)Online publication date: May-2023
https://doi.org/10.1109/RoSE59155.2023.00009
Taha MBata FDweik OAtallah R(2023)A Real-Time Crowd Estimation Based on RSSI Measurements Using Convolutional Neural Network2023 IEEE 11th Conference on Systems, Process & Control (ICSPC)10.1109/ICSPC59664.2023.10420089(159-164)Online publication date: 16-Dec-2023
https://doi.org/10.1109/ICSPC59664.2023.10420089
Show More Cited By

Recommendations

Data-Driven Evaluation of HVAC Systems in Commercial Buildings and Identification of Savings Opportunities
Assessment of Energy Savings Related to Sustainable Solutions Improving Summer Comfort
Energy Saving Recommendations and User Location Modeling in Commercial Buildings
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

Commercial buildings consume a large portion of the total electricity in the United States. One method for energy saving in commercial buildings targets inefficiencies of unoccupied spaces by relaxing the setpoint temperature. However, energy savings ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

November 2016

273 pages

ISBN:9781450342643

DOI:10.1145/2993422

Copyright © 2016 ACM.

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 the author(s) 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

BuildSys '16

Sponsor:

BuildSys '16: The 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

November 16 - 17, 2016

CA, Palo Alto, USA

Acceptance Rates

Overall Acceptance Rate 148 of 500 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

51
Total Citations
View Citations
644
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)1

Reflects downloads up to 28 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Kolvig-Raun EKjærgaard MBrorsen R(2023)EDDE: An Event-Driven Data Exchange to Accurately Introspect Cobot Applications2023 IEEE/ACM 5th International Workshop on Robotics Software Engineering (RoSE)10.1109/RoSE59155.2023.00009(25-30)Online publication date: May-2023
https://doi.org/10.1109/RoSE59155.2023.00009
Taha MBata FDweik OAtallah R(2023)A Real-Time Crowd Estimation Based on RSSI Measurements Using Convolutional Neural Network2023 IEEE 11th Conference on Systems, Process & Control (ICSPC)10.1109/ICSPC59664.2023.10420089(159-164)Online publication date: 16-Dec-2023
https://doi.org/10.1109/ICSPC59664.2023.10420089
Rajabi HHu ZDing XPan SDu WCerpa ALehnhoff SIrwin DWang D(2022)MODESProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538852(228-239)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3538637.3538852
Bakar ARahman TMontanari ALei JShafik RKawsar FLiKamWa RHengartner U(2022)Logic-based intelligence for batteryless sensorsProceedings of the 23rd Annual International Workshop on Mobile Computing Systems and Applications10.1145/3508396.3512870(22-28)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3508396.3512870
Golestan SStroulia ENikolaidis I(2022)Smart Indoor Space Simulation Methodologies: A ReviewIEEE Sensors Journal10.1109/JSEN.2022.315920522:9(8337-8359)Online publication date: 1-May-2022
https://doi.org/10.1109/JSEN.2022.3159205
Ma XXi WZhao XChen ZZhang HZhao J(2022)Wisual: Indoor Crowd Density Estimation and Distribution Visualization Using Wi-FiIEEE Internet of Things Journal10.1109/JIOT.2021.31195429:12(10077-10092)Online publication date: 15-Jun-2022
https://doi.org/10.1109/JIOT.2021.3119542
He WMa YFan CWang XHuang B(2022)Non-intrusive Indoor Occupancy Detection Methods Based on Machine Learning TechniquesProceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate10.1007/978-981-19-5256-2_93(1186-1201)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-981-19-5256-2_93
Zhang LZhang YWang BZheng XYang L(2021)WiCrowd: Counting the Directional Crowd With a Single Wireless LinkIEEE Internet of Things Journal10.1109/JIOT.2020.30478688:10(8644-8656)Online publication date: 15-May-2021
https://doi.org/10.1109/JIOT.2020.3047868
Jana RDey SDasgupta P(2020)A Hierarchical HVAC Control Scheme for Energy-aware Smart Building AutomationACM Transactions on Design Automation of Electronic Systems10.1145/339366625:4(1-33)Online publication date: 23-May-2020
https://dl.acm.org/doi/10.1145/3393666
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents