A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improving Occupant Thermal Comfort through Personalized Space Recommendation
https://orcid.org/0000-0003-0539-1939
Thermal comfort significantly affects occupants’ satisfaction, well-being, and productivity in built environments. To improve thermal comfort, existing literature has investigated human-in-the-loop HVAC control and personal comfort systems (PCS) to adjust the macro- and microenvironments surrounding occupants to meet their preferences. However, these methods have limitations, including the inability to satisfy all occupants, energy waste due to undesired fluctuations in HVAC settings, uncertainty in energy savings, high upfront costs, lack of scalability, to name a few. In contrast, this study proposed a SpaceMatch framework by reimagining occupants as mobile agents who are willing to move to spaces where the conditions best match their personal preferences and needs. This framework leverages personal comfort models developed for each occupant using machine learning and natural spatial-temporal temperature variations in buildings to make space recommendations. An experiment with 12 subjects was conducted in a testbed building from October to November 2021 at Clemson University to validate the proposed framework. The results showed that subjects’ thermal comfort increased at least by 18.8% with the help of space recommendations compared to the baseline, without incurring additional energy use. This framework has great potential in many built environments where flexible workplace strategies are employed, such as open-plan offices and libraries, especially in the postpandemic era when people’s working habits have significantly changed because of remote work, job autonomy, and flexible scheduling.
The SpaceMatch framework proposed in this study is expected to fundamentally transform the way buildings are operated in a more flexible, comfortable, and sustainable direction that better supports their occupants’ thermal preferences and needs. The proposed framework aims to leverage buildings’ natural spatial-temporal temperature variations that result from their layouts, occupancy levels, the time of day, and so on, or synthetic variations from thermostat setbacks to accommodate each occupant. The goal is to reduce the response time, conflicts in individual preferences, and energy use. This new concept embraces the future workspaces of the postpandemic era, when increased job autonomy, flexible work schedules and locations, and coworking spaces will become the “new normal,” driving facilities managers to rethink their operations strategies to promote comfort, equity, and social justice for indoor environments among occupants of different genders, ages, cultures, and ethnicities.
Improving Occupant Thermal Comfort through Personalized Space Recommendation
https://orcid.org/0000-0003-0539-1939
Thermal comfort significantly affects occupants’ satisfaction, well-being, and productivity in built environments. To improve thermal comfort, existing literature has investigated human-in-the-loop HVAC control and personal comfort systems (PCS) to adjust the macro- and microenvironments surrounding occupants to meet their preferences. However, these methods have limitations, including the inability to satisfy all occupants, energy waste due to undesired fluctuations in HVAC settings, uncertainty in energy savings, high upfront costs, lack of scalability, to name a few. In contrast, this study proposed a SpaceMatch framework by reimagining occupants as mobile agents who are willing to move to spaces where the conditions best match their personal preferences and needs. This framework leverages personal comfort models developed for each occupant using machine learning and natural spatial-temporal temperature variations in buildings to make space recommendations. An experiment with 12 subjects was conducted in a testbed building from October to November 2021 at Clemson University to validate the proposed framework. The results showed that subjects’ thermal comfort increased at least by 18.8% with the help of space recommendations compared to the baseline, without incurring additional energy use. This framework has great potential in many built environments where flexible workplace strategies are employed, such as open-plan offices and libraries, especially in the postpandemic era when people’s working habits have significantly changed because of remote work, job autonomy, and flexible scheduling.
The SpaceMatch framework proposed in this study is expected to fundamentally transform the way buildings are operated in a more flexible, comfortable, and sustainable direction that better supports their occupants’ thermal preferences and needs. The proposed framework aims to leverage buildings’ natural spatial-temporal temperature variations that result from their layouts, occupancy levels, the time of day, and so on, or synthetic variations from thermostat setbacks to accommodate each occupant. The goal is to reduce the response time, conflicts in individual preferences, and energy use. This new concept embraces the future workspaces of the postpandemic era, when increased job autonomy, flexible work schedules and locations, and coworking spaces will become the “new normal,” driving facilities managers to rethink their operations strategies to promote comfort, equity, and social justice for indoor environments among occupants of different genders, ages, cultures, and ethnicities.
Improving Occupant Thermal Comfort through Personalized Space Recommendation
https://orcid.org/0000-0003-0539-1939
Thermal comfort significantly affects occupants’ satisfaction, well-being, and productivity in built environments. To improve thermal comfort, existing literature has investigated human-in-the-loop HVAC control and personal comfort systems (PCS) to adjust the macro- and microenvironments surrounding occupants to meet their preferences. However, these methods have limitations, including the inability to satisfy all occupants, energy waste due to undesired fluctuations in HVAC settings, uncertainty in energy savings, high upfront costs, lack of scalability, to name a few. In contrast, this study proposed a SpaceMatch framework by reimagining occupants as mobile agents who are willing to move to spaces where the conditions best match their personal preferences and needs. This framework leverages personal comfort models developed for each occupant using machine learning and natural spatial-temporal temperature variations in buildings to make space recommendations. An experiment with 12 subjects was conducted in a testbed building from October to November 2021 at Clemson University to validate the proposed framework. The results showed that subjects’ thermal comfort increased at least by 18.8% with the help of space recommendations compared to the baseline, without incurring additional energy use. This framework has great potential in many built environments where flexible workplace strategies are employed, such as open-plan offices and libraries, especially in the postpandemic era when people’s working habits have significantly changed because of remote work, job autonomy, and flexible scheduling.
The SpaceMatch framework proposed in this study is expected to fundamentally transform the way buildings are operated in a more flexible, comfortable, and sustainable direction that better supports their occupants’ thermal preferences and needs. The proposed framework aims to leverage buildings’ natural spatial-temporal temperature variations that result from their layouts, occupancy levels, the time of day, and so on, or synthetic variations from thermostat setbacks to accommodate each occupant. The goal is to reduce the response time, conflicts in individual preferences, and energy use. This new concept embraces the future workspaces of the postpandemic era, when increased job autonomy, flexible work schedules and locations, and coworking spaces will become the “new normal,” driving facilities managers to rethink their operations strategies to promote comfort, equity, and social justice for indoor environments among occupants of different genders, ages, cultures, and ethnicities.
Improving Occupant Thermal Comfort through Personalized Space Recommendation
J. Comput. Civ. Eng.
Huang, Yu (author) / Gadde, Rohit Sai Kiran (author) / Lopes, Snowil (author) / Li, Da (author) / McGuire, Brendan (author)
2023-01-01
Article (Journal)
Electronic Resource
English
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