research-article

Occupancy based demand response HVAC control strategy

Authors:

Varick L. Erickson,

Alberto E. CerpaAuthors Info & Claims

BuildSys '10: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building

Pages 7 - 12

https://doi.org/10.1145/1878431.1878434

Published: 02 November 2010 Publication History

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Abstract

Heating, cooling and ventilation accounts for 30% energy usage and for 50% of the electricity usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. This paper proposes an HVAC control strategy based on occupancy prediction and real time occupancy monitoring via a sensor network of cameras. This strategy shows 20.0% potential energy savings while still maintaining ASHRAE building standards.

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V. L. Erickson, Y. Lin, A. Kamthe, R. Brahme, A. Cerpa, M. D. Sohn, and S. Narayanan. Energy efficient building environment control strategies using real-time occupancy measurements. In Proceedings of the 1st ACM Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings (BuildSys 2009) in conjunction with ACM SenSys 2009, Berkeley, CA, USA, Nov. 2009. ACM.

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Cited By

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Bai YLi CPrasad Jenne SZhang SWang J(2024)Thermal comfort driven space heating control via hierarchical state machine strategy interacting with multiphysics simulationEnergy and Buildings10.1016/j.enbuild.2024.114806323(114806)Online publication date: Nov-2024
https://doi.org/10.1016/j.enbuild.2024.114806
Lan BZhang RYu ZLin BHuang G(2024)An Artificial Neural Network Based Approach to Air Supply Control in Large Indoor Spaces Considering Occupancy DynamicsBuilding and Environment10.1016/j.buildenv.2024.111864(111864)Online publication date: Jul-2024
https://doi.org/10.1016/j.buildenv.2024.111864
Kaneko NOkazawa KZhao DNishikawa HTaniguchi IMurayama HYura YOkamoto MCatthoor FOnoye T(2024)Non-intrusive thermal load disaggregation and forecasting for effective HVAC systemsApplied Energy10.1016/j.apenergy.2024.123379367(123379)Online publication date: Aug-2024
https://doi.org/10.1016/j.apenergy.2024.123379
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Index Terms

Occupancy based demand response HVAC control strategy
1. Applied computing
  1. Computers in other domains
  2. Operations research
    1. Industry and manufacturing
      1. Command and control
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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Information

Published In

BuildSys '10: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building

November 2010

93 pages

ISBN:9781450304580

DOI:10.1145/1878431

General Chair:
Antonio Ruzzelli
University College Dublin, Ireland

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2010

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SenSys10

Sponsor:

SenSys10: The 8th ACM Conference on Embedded Network Sensor Systems

November 2, 2010

Zurich, Switzerland

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

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Bai YLi CPrasad Jenne SZhang SWang J(2024)Thermal comfort driven space heating control via hierarchical state machine strategy interacting with multiphysics simulationEnergy and Buildings10.1016/j.enbuild.2024.114806323(114806)Online publication date: Nov-2024
https://doi.org/10.1016/j.enbuild.2024.114806
Lan BZhang RYu ZLin BHuang G(2024)An Artificial Neural Network Based Approach to Air Supply Control in Large Indoor Spaces Considering Occupancy DynamicsBuilding and Environment10.1016/j.buildenv.2024.111864(111864)Online publication date: Jul-2024
https://doi.org/10.1016/j.buildenv.2024.111864
Kaneko NOkazawa KZhao DNishikawa HTaniguchi IMurayama HYura YOkamoto MCatthoor FOnoye T(2024)Non-intrusive thermal load disaggregation and forecasting for effective HVAC systemsApplied Energy10.1016/j.apenergy.2024.123379367(123379)Online publication date: Aug-2024
https://doi.org/10.1016/j.apenergy.2024.123379
Jacob JPandit DSen J(2024)Protocols for planning micro-zones to facilitate occupant-centric control (OCC) to reduce HVAC energy consumption in Indian open-plan officesEnergy Efficiency10.1007/s12053-024-10271-417:8Online publication date: 30-Oct-2024
https://doi.org/10.1007/s12053-024-10271-4
Malatesta TLi QBreadsell JEon C(2023)Distinguishing Household Groupings within a Precinct Based on Energy Usage Patterns Using Machine Learning AnalysisEnergies10.3390/en1610411916:10(4119)Online publication date: 16-May-2023
https://doi.org/10.3390/en16104119
Talami RDawoodjee IGhahramani A(2023)Quantifying Energy Savings from Optimal Selection of HVAC Temperature Setpoints and Setbacks across Diverse Occupancy Rates and PatternsBuildings10.3390/buildings1312299813:12(2998)Online publication date: 30-Nov-2023
https://doi.org/10.3390/buildings13122998
Chhaglani BZakaria CGummeson JShenoy PHossein Motlagh NZaidan MRebeiro-Hargrave AArvind D(2023)BreathEasy: Exploring the Potential of Acoustic Sensing for Healthy Indoor EnvironmentsProceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3597064.3597338(25-30)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3597064.3597338
Liu YDong BHong TOlesen BLawrence TO’Neill Z(2023)ASHRAE URP-1883: Development and Analysis of the ASHRAE Global Occupant Behavior DatabaseScience and Technology for the Built Environment10.1080/23744731.2023.223597129:8(749-781)Online publication date: 26-Jul-2023
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Jacob JPandit DSen J(2023)Energy-saving potential in Indian open-plan offices using Micro-Zonal Occupant Centric Control (MZOCC)Energy and Buildings10.1016/j.enbuild.2023.112799282(112799)Online publication date: Mar-2023
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Wu DMomeni MRazban AChen J(2023)Optimizing demand-controlled ventilation with thermal comfort and CO2 concentrations using long short-term memory and genetic algorithmBuilding and Environment10.1016/j.buildenv.2023.110676243(110676)Online publication date: Sep-2023
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