skip to main content
10.1145/2939912.2942355acmotherconferencesArticle/Chapter ViewAbstractPublication Pagese-energyConference Proceedingsconference-collections
poster

Multiple time-scale model predictive control for thermal comfort in buildings

Published: 21 June 2016 Publication History

Abstract

Intelligent control of heating, ventilation, and air conditioning (HVAC) systems in commercial buildings have been extensively studied in the literature. Although prior work has shown the benefits of using Model Predictive Control (MPC), existing work falls short either by relying on linear HVAC models or using MPC assuming control actions at a single (hourly) time scale, although more frequent control is feasible for some HVAC elements. Our main contribution is the use of a bi-linear thermal model and the careful modeling of the multiple time-scales inherent in the operation of an HVAC system, which permits the design of a multiple time-scale MPC control. We find that employing a multiple time-scale MPC results in significantly better comfort in comparison to a single time-scale MPC, typically without an increase in power consumption. Moreover, there exist cases where there is a significant reduction in power consumption (40%) for the two time-scale MPC in comparison to the single time-scale, with no decrease in comfort.

References

[1]
Arguello-Serrano, B., and Vélez-Reyes, M. Nonlinear control of a heating, ventilating, and air conditioning system with thermal load estimation. Control Systems Technology, IEEE Transactions on 7, 1 (1999), 56--63.
[2]
Aswani, A., Master, N., Taneja, J., Krioukov, A., Culler, D., and Tomlin, C. Energy-efficient building HVAC control using hybrid system LBMPC. arXiv preprint arXiv:1204.4717 (2012).
[3]
Kwadzogah, R., Zhou, M., and Li, S. Model predictive control for HVAC systems - A review. In Automation Science and Engineering (CASE), 2013 IEEE International Conference on (2013), IEEE, pp. 442--447.
[4]
Maasoumy, M. Modeling and optimal control algorithm design for HVAC systems in energy efficient buildings. Master's thesis, EECS Dept., Univ of California, Berkeley (2014).
[5]
Nassif, N., Kajl, S., and Sabourin, R. Optimization of HVAC control system strategy using two-objective genetic algorithm. HVAC&R Research 11, 3 (2005), 459--486.
[6]
Zheng, G., and Zaheer-Uddin, M. Optimization of thermal processes in a variable air volume HVAC system. Energy 21, 5 (1996), 407--420.

Cited By

View all
  • (2024)Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial BuildingsIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33668695:8(4280-4292)Online publication date: Aug-2024
  • (2021)A Review of Deep Reinforcement Learning for Smart Building Energy ManagementIEEE Internet of Things Journal10.1109/JIOT.2021.30784628:15(12046-12063)Online publication date: 1-Aug-2021
  • (2018)On the interaction between personal comfort systems and centralized HVAC systems in office buildingsAdvances in Building Energy Research10.1080/17512549.2018.150565414:1(129-157)Online publication date: 12-Aug-2018

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
e-Energy '16: Proceedings of the Seventh International Conference on Future Energy Systems Poster Sessions
June 2016
24 pages
ISBN:9781450344173
DOI:10.1145/2939912
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2016

Check for updates

Qualifiers

  • Poster

Conference

e-Energy'16

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial BuildingsIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33668695:8(4280-4292)Online publication date: Aug-2024
  • (2021)A Review of Deep Reinforcement Learning for Smart Building Energy ManagementIEEE Internet of Things Journal10.1109/JIOT.2021.30784628:15(12046-12063)Online publication date: 1-Aug-2021
  • (2018)On the interaction between personal comfort systems and centralized HVAC systems in office buildingsAdvances in Building Energy Research10.1080/17512549.2018.150565414:1(129-157)Online publication date: 12-Aug-2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media