1 / 22

Bing Dong 1 , Yifei Duan 1 , Rui Liu 2 , Taeg Nishimoto 2

The Impact of Occupancy Behavior Patterns On the Energy Consumption in Low-income Residential Buildings. Bing Dong 1 , Yifei Duan 1 , Rui Liu 2 , Taeg Nishimoto 2 1 Building Performance and Diagnostics Group, Mechanical Engineering, the University of Texas, San Antonio, TX, USA

newton
Télécharger la présentation

Bing Dong 1 , Yifei Duan 1 , Rui Liu 2 , Taeg Nishimoto 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Impact of Occupancy Behavior Patterns On the Energy Consumption in Low-income Residential Buildings Bing Dong1, Yifei Duan1, Rui Liu2, Taeg Nishimoto2 1 Building Performance and Diagnostics Group, Mechanical Engineering, the University of Texas, San Antonio, TX, USA 2 Collegeof Architecture, the University of Texas, San Antonio, TX, USA

  2. Introduction • Large gaps between measured performance and simulated results Source: NBI report 2008 Energy Performance of LEED For New Construction Buildings

  3. Introduction • Occupancy behavior (OB) has significant influence on building energy use

  4. Introduction • People spend most of time at homes Based on American time user survey data (ATUS)

  5. Introduction • Occupancy behavior is a key factor influencing building energy consumption and indoor environment Climate Condition Energy Consumption Building Building Envelope Occupancy Behavior Building Systems Occupancy Operation Occupancy Presence Occupancy Activities

  6. UTSA Occupancy Test-beds • “Three+1” project for Westside low income houses • A collaborative project of UTSA the San Antonio Alternative Housing Corporation, and the Texas Department of Housing and Community Affairs • Honorable Mention for Research and Education in Residential Construction, presented by City of San Antonio Green Building Awards, 2013

  7. Introduction AAC House 1,019sf Container House 1,106sf SIPs House 1,073sf Stick House 1,000sf

  8. Instrumentation Nonintrusive Sensor Network Temperature Sensor Powerhouse Dynamics e-Monitor

  9. Energy Consumption Total Monthly Energy Consumption Stick # of Occupants at homes 2 4 4 2 3

  10. Behavior 1: Thermostat Schedule DOE Benchmark August 12 to August 19, 2013 All four houses thermostat schedule

  11. Behavior 1: Thermostat Schedule HVAC working status for 1 week SIP house AAC house On Off

  12. Behavior 1: Thermostat Schedule Energy Consumption of HVAC for 1 week(12/8-19/8) Energy Consumption (kWh)

  13. Behavior 2: Usage of Major Appliances Cooling and Heating 45% Building Energy Data Book (2009) Energy Consumption of Stick House for 5 months

  14. Behavior 2: Usage of Major Appliances (Water Heater) Energy Consumption of Water Heater for 1 week(12/8-19/8) Energy Consumption (kWh)

  15. Behavior 2: Usage of Major Appliances (Water Heater) SIP Stick ATUS

  16. Behavior 3: Occupancy Movement Occupancy movement in SIP house Living Room Temperature Profiles of living room and master bedroom of SIP house

  17. Behavior 3: Occupancy Movement High Probability Living Room in SIP house (aggregated one week data)

  18. Behavior 3: Occupancy Movement Kitchen in SIP house (aggregated one week data)

  19. Integrate with Energy Models Appliances Energy Saving: 15% Comfort time Increase: 25% New Thermostat Schedule Occupancy Movement Patterns Building Controls Virtual Test bed (LBNL) Measured Energy and Temperature Data

  20. Conclusion and Future Work • In this study, we present occupancy behavior and energy usage patterns in four low income houses • We also demonstrate possible energy savings based on occupancy movement • In future studies, we will: • Develop statistical models to describe occupancy behavior in buildings. • Integrate with energy consumption patterns

  21. IEA Annex 66 • IEA Annex 66 “Definition and Simulation of Occupant Behavior in Buildings”. UTSA BPD group is task leader of subtask 1. 23 countries and regions

  22. Acknowledgement

More Related