Occupant-centric control (OCC) strategies rely on different algorithms to learn and predict occupants’ patterns and preferences, then utilize these predictions to optimize building operations. However, testing differ...Occupant-centric control (OCC) strategies rely on different algorithms to learn and predict occupants’ patterns and preferences, then utilize these predictions to optimize building operations. However, testing different OCC algorithms or fine-tuning their configurations in real buildings can be a lengthy process. To this end, we present a framework for testing OCCs in a simulation environment prior to field implementation. The proposed workflow entails using synthetic occupant behaviour models and simulating OCC strategies to learn their preferences. The goal is to enable quick comparison of different OCC configurations under various scenarios by modifying occupant behaviour assumptions, as well as climate and design parameters. For proof-of-concept, the proposed method was applied in a case-study to simulate OCCs for lighting and heating/cooling setpoint adjustments in a single office under various occupant types, as well as OCC settings and design configurations. Results demonstrated the benefits of the proposed framework and its potential for providing a more holistic evaluation of OCCs under different scenarios. Using the proposed framework, building designers and operators can identify potential issues with OCCs and fine-tune their settings prior to field implementation.展开更多
基金This research was supported by Concordia University’s Dean of the Faculty of Engineering and Computer Science Start-up funds program and Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grant RGPIN-2020-06804The authors would like to acknowledge the contributions of Mr.Erik Bowden.This work was also developed thanks to the excellent research networking provided by IEA EBC Annex 79“Occupant-Centric Building Design and Operation”.
文摘Occupant-centric control (OCC) strategies rely on different algorithms to learn and predict occupants’ patterns and preferences, then utilize these predictions to optimize building operations. However, testing different OCC algorithms or fine-tuning their configurations in real buildings can be a lengthy process. To this end, we present a framework for testing OCCs in a simulation environment prior to field implementation. The proposed workflow entails using synthetic occupant behaviour models and simulating OCC strategies to learn their preferences. The goal is to enable quick comparison of different OCC configurations under various scenarios by modifying occupant behaviour assumptions, as well as climate and design parameters. For proof-of-concept, the proposed method was applied in a case-study to simulate OCCs for lighting and heating/cooling setpoint adjustments in a single office under various occupant types, as well as OCC settings and design configurations. Results demonstrated the benefits of the proposed framework and its potential for providing a more holistic evaluation of OCCs under different scenarios. Using the proposed framework, building designers and operators can identify potential issues with OCCs and fine-tune their settings prior to field implementation.
