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Energy efficient building environment control strategies using real-time occupancy measurements

Energy efficient building environment control strategies using real-time occupancy measurements Energy Ef cient Building Environment Control Strategies Using Real-time Occupancy Measurements Varick L. Erickson1 , Yiqing Lin2 , Ankur Kamthe1 ,Rohini Brahme2 , Amit Surana2 , Alberto E. Cerpa1 , Michael D. Sohn3 and Satish Narayanan2 1 University 2 United of California - Merced {verickson,akamthe,acerpa@ucmerced.edu} Technologies Research Center {LinY,BrahmeR,SuranaA,NarayaS@utrc.utc.com} 3 Lawrence Berkeley National Laboratory {MDSohn@lbl.gov} Abstract Current climate control systems often rely on building regulation maximum occupancy numbers for maintaining proper temperatures. However, in many situations, there are rooms that are used infrequently, and may be heated or cooled needlessly. Having knowledge regarding occupancy and being able to accurately predict usage patterns may allow signi cant energy-savings by intelligent control of the L-HVAC systems. In this paper, we report on the deployment of a wireless camera sensor network for collecting data regarding occupancy in a large multi-function building. The system estimates occupancy with an accuracy of 80%. Using data collected from this system, we construct multivariate Gaussian and agent based models for predicting user mobility patterns in buildings. Using these models, we can predict room usage thereby enabling us to control the HVAC systems in an adaptive manner. Our simulations indicate a 14% reduction in HVAC energy usage http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Energy efficient building environment control strategies using real-time occupancy measurements

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Datasource
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISBN
978-1-60558-824-7
doi
10.1145/1810279.1810284
Publisher site
See Article on Publisher Site

Abstract

Energy Ef cient Building Environment Control Strategies Using Real-time Occupancy Measurements Varick L. Erickson1 , Yiqing Lin2 , Ankur Kamthe1 ,Rohini Brahme2 , Amit Surana2 , Alberto E. Cerpa1 , Michael D. Sohn3 and Satish Narayanan2 1 University 2 United of California - Merced {verickson,akamthe,acerpa@ucmerced.edu} Technologies Research Center {LinY,BrahmeR,SuranaA,NarayaS@utrc.utc.com} 3 Lawrence Berkeley National Laboratory {MDSohn@lbl.gov} Abstract Current climate control systems often rely on building regulation maximum occupancy numbers for maintaining proper temperatures. However, in many situations, there are rooms that are used infrequently, and may be heated or cooled needlessly. Having knowledge regarding occupancy and being able to accurately predict usage patterns may allow signi cant energy-savings by intelligent control of the L-HVAC systems. In this paper, we report on the deployment of a wireless camera sensor network for collecting data regarding occupancy in a large multi-function building. The system estimates occupancy with an accuracy of 80%. Using data collected from this system, we construct multivariate Gaussian and agent based models for predicting user mobility patterns in buildings. Using these models, we can predict room usage thereby enabling us to control the HVAC systems in an adaptive manner. Our simulations indicate a 14% reduction in HVAC energy usage

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