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A user-interactive system for smart thermal environment control in office buildings

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Listed:
  • Liu, Xiaoqi
  • Lee, Seungjae
  • Bilionis, Ilias
  • Karava, Panagiota
  • Joe, Jaewan
  • Sadeghi, Seyed Amir

Abstract

In this paper, our goal is to develop user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the model-predictive control (MPC) strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the HVAC energy consumption with MPC can be underestimated by up to 55% without considering occupants’ overrides. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios.

Suggested Citation

  • Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921004736
    DOI: 10.1016/j.apenergy.2021.117005
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    References listed on IDEAS

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    Cited by:

    1. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).

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