research-article

Thermovote: participatory sensing for efficient building HVAC conditioning

Authors:

Varick L. Erickson,

Alberto E. CerpaAuthors Info & Claims

BuildSys '12: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings

Pages 9 - 16

https://doi.org/10.1145/2422531.2422534

Published: 06 November 2012 Publication History

Abstract

Thermal comfort has traditionally been measured solely by temperature. While other methods such as Predicted Mean Vote (PMV) are available for measuring thermal comfort, the parameters required for an accurate value are overly complicated to obtain and require a great deal of sensory input. This paper proposes to bypass overly cumbersome or simplistic measures thermal comfort by bringing humans in the loop. By using humans as sensors, we can accurately adjust temperatures to improve occupant comfort. We show that occupants are more comfortable with a system that continually adjusts to thermal preference than a system that attempts to predict user comfort based on environmental factors. In addition, we also show that such a system is able to save 10.1% energy while improving the quality of service.

References

[1]

Appliance data energy use. http://minotaur.lbl.gov/aeud/appliances/space-heater/delonghi/1193/.

[2]

ASHRAE standard 55: Thermal environmental conditions for human occupancy. ASHRAE, Inc., 2004.

[3]

Y. Agarwal, B. Balaji, S. Dutta, R. K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next frontier in HVAC control. In IPSN'11.

[4]

G. Brager, M. Fountain, C. Benton, E. A. Arens, and F. Bauman. A comparison of methods for assessing thermal sensation and acceptability in the field. Energy and Buildings, 1993.

[5]

V. L. Erickson, M. Á. Carreira-Perpiñán, and A. E. Cerpa. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In IPSN'11.

[6]

B. A. D. C. P. for Building Automation and C. Networks. http://www.bacnet.org/.

[7]

B.-G. B. Jazizadeh F, Kavulya G, Klein L. Continous sensing of occupant perception of indoor ambient factors. ASCE Workshop of Computing in Civil Engineering, 2011.

[8]

P. Kunze and J. Grunewald. Suitable algorithms for practical assessment of indoor climates in hospital wards. Symposium on Building Physics, Sept. 2010.

[9]

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, 2010.

Digital Library

[10]

F. P. O. Thermal comfort. Analysis and applications in environmental engineering. Danish Technical Press, 1970.

[11]

M. Paciuk. The Role of Personal Control of the Environment in Thermal Comfort and Satisfaction at the Workplace. Environmental Design Research Association, 1990.

[12]

J. U. Pfafferott, S. Herkel, D. E. Kalz, and A. Zeuschner. Comparison of low-energy office buildings in summer using different thermal comfort criteria. Energy & Buildings, 2007.

[13]

M. Rawi and A. Al-Anbuky. Passive house sensor networks: Human centric thermal comfort concept. In ISSNIP, 2009.

[14]

M. Schumann, A. Burillo, and N. Wilson. Predicting the desired thermal comfort conditions for shared offices. In ICCCBE, 2010.

[15]

G. Ye, C. Yang, Y. Chen, and Y. Li. A new approach for measuring predicted mean vote (PMV) and standard effective temperature (SET). Building and Environment, 2003.

Cited By

Yano TSako M(2024)Energy-Saving Occupant-Feedback Control Method Under Preferred Air-Conditioner Settings of OccupantsIEEE Access10.1109/ACCESS.2024.336795412(29126-29143)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3367954
Upasani NGuerra-Santin OMohammadi M(2024)Developing Building-Specific, Occupant-Centric Thermal Comfort Models: A Methodological ApproachJournal of Building Engineering10.1016/j.jobe.2024.110281(110281)Online publication date: Jul-2024
https://doi.org/10.1016/j.jobe.2024.110281
Soleimanijavid AKonstantzos ILiu X(2024)Challenges and opportunities of occupant-centric building controls in real-world implementation: A critical reviewEnergy and Buildings10.1016/j.enbuild.2024.113958(113958)Online publication date: Feb-2024
https://doi.org/10.1016/j.enbuild.2024.113958
Show More Cited By

Index Terms

Thermovote: participatory sensing for efficient building HVAC conditioning
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

A Human Thermal Comfort Level Estimating Method Using Thermal Image and Sensor Data
iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence

Recently, it has become a trend to construct a thermal environment with human thermal comfort. The advantage of human thermal comfort is that we could adjust the environment through the thermal sensation of human. Since human thermal comfort is ...
A thermal comfort levels investigation of a naturally ventilated and air-conditioned office
SMO'08: Proceedings of the 8th conference on Simulation, modelling and optimization

The purpose of this study is to investigate thermal comfort levels of a naturally ventilated and air-conditioner office. Field experiments conducted in an office room in Universiti Putra Malaysia (UPM) used survey questionnaires and physical ...
Contribution of Electrophysiological Signals in the Study of Thermal Comfort: a Case Study
ICBRA '23: Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications

Thermal comfort is defined as a mental condition that expresses satisfaction with thermal environments, therefore being subjectively assessed by the individual. Nonetheless, thermal comfort (or discomfort) causes a physiological response that may be ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

BuildSys '12: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings

November 2012

227 pages

ISBN:9781450311700

DOI:10.1145/2422531

General Chair:
George J. Pappas
University of Pennsylvania

Copyright © 2012 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 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SenSys '12

Sponsor:

SenSys '12: The 10th ACM Conference on Embedded Network Sensor Systems

November 6, 2012

Ontario, Toronto, Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

115
Total Citations
View Citations
788
Total Downloads

Downloads (Last 12 months)45
Downloads (Last 6 weeks)13

Reflects downloads up to 29 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yano TSako M(2024)Energy-Saving Occupant-Feedback Control Method Under Preferred Air-Conditioner Settings of OccupantsIEEE Access10.1109/ACCESS.2024.336795412(29126-29143)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3367954
Upasani NGuerra-Santin OMohammadi M(2024)Developing Building-Specific, Occupant-Centric Thermal Comfort Models: A Methodological ApproachJournal of Building Engineering10.1016/j.jobe.2024.110281(110281)Online publication date: Jul-2024
https://doi.org/10.1016/j.jobe.2024.110281
Soleimanijavid AKonstantzos ILiu X(2024)Challenges and opportunities of occupant-centric building controls in real-world implementation: A critical reviewEnergy and Buildings10.1016/j.enbuild.2024.113958(113958)Online publication date: Feb-2024
https://doi.org/10.1016/j.enbuild.2024.113958
Emad-Ud-Din MWang Y(2023)Indoor Occupancy Sensing via Networked Nodes (2012–2022): A ReviewFuture Internet10.3390/fi1503011615:3(116)Online publication date: 22-Mar-2023
https://doi.org/10.3390/fi15030116
Skaloumpakas PSarmas EMylona ZCavadenti ASantori FMarinakis V(2023)Predicting Thermal Comfort in Buildings With Machine Learning and Occupant Feedback2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv)10.1109/MetroLivEnv56897.2023.10164051(34-39)Online publication date: 29-May-2023
https://doi.org/10.1109/MetroLivEnv56897.2023.10164051
Rosenberger JGuo ZCoffman AAgdas DBarooah P(2022)An Open-Source Platform for Indoor Environment Monitoring with Participatory Comfort SensingSensors10.3390/s2301036423:1(364)Online publication date: 29-Dec-2022
https://doi.org/10.3390/s23010364
Ramallo-González ABardaki CKotsopoulos DTomat VGonzález Vidal AFernandez Ruiz PSkarmeta Gómez A(2022)Reducing Energy Consumption in the Workplace via IoT-Allowed Behavioural Change InterventionsBuildings10.3390/buildings1206070812:6(708)Online publication date: 24-May-2022
https://doi.org/10.3390/buildings12060708
Zheng ZLuo PLi YLuo SJian JHuang ZLehnhoff SIrwin DWang D(2022)Towards lifelong thermal comfort prediction with KubeEdge-sednaProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538856(263-276)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3538637.3538856
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
Pandharipande ALankhorst MFrimout E(2022)Luminaire-Based Multi-Modal Sensing for Environmental Building ApplicationsIEEE Sensors Journal10.1109/JSEN.2021.313754222:3(2564-2571)Online publication date: 1-Feb-2022
https://doi.org/10.1109/JSEN.2021.3137542
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