A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Collective comfort optimization in multi-occupancy environments by leveraging personal comfort models and thermal distribution patterns
https://orcid.org/0000-0002-0561-359X
https://orcid.org/0000-0003-4734-0750
https://orcid.org/0000-0001-5888-4265
Abstract Buildings with shared spaces present a unique challenge for maintaining thermal comfort due to their dynamic occupancy patterns and the potential for wide variation in occupant thermal preferences and tolerances. Conventional approaches aim to achieve relatively uniform temperature conditions throughout a space, which implies that the thermal environment will likely be suboptimal for many of the individual occupants. Recent research efforts have integrated personal comfort models with heating, ventilation and air conditioning (HVAC) controls and have shown promising improvements by taking a highly individualistic approach to evaluating thermal comfort and adjusting HVAC operations accordingly. In this work, we aim to further advance occupant-centric controls by evaluating the benefits that could be gained by explicitly influencing and leveraging the development of non-uniform thermal conditions within a space. In particular, we consider the context of a multi-occupancy open office space shared by six occupants, where the thermal distribution patterns can be influenced by controlling the direction and flow rate of conditioned air being supplied through a central diffuser. Computational fluid dynamics was used to model and simulate thermal distribution patterns. Six probabilistic thermal comfort profiles were used to quantify the likelihood of each occupant being comfortable under the various control settings and location assignments. We analyzed three control strategies with incremental complexity, and collective comfort probability was shown to improve by 11%, 22%, and 30%, respectively. Our results also showed the potential energy-saving pathway by altering supply airflow direction instead of changing supply airflow rate to adjust thermal conditions in shared environments.
Highlights Collective comfort probability can be improved by leveraging thermal distribution patterns and personal comfort models. Occupants are subjected to different thermal conditions in multi-occupancy spaces. Thermal comfort of occupants should be assessed based on their micro-locations in shared spaces. Adjusting supply airflow direction can be used as an alternative strategy for adjusting thermal conditions. Thermal distribution patterns can be analyzed with CFD simulations.
Collective comfort optimization in multi-occupancy environments by leveraging personal comfort models and thermal distribution patterns
https://orcid.org/0000-0002-0561-359X
https://orcid.org/0000-0003-4734-0750
https://orcid.org/0000-0001-5888-4265
Abstract Buildings with shared spaces present a unique challenge for maintaining thermal comfort due to their dynamic occupancy patterns and the potential for wide variation in occupant thermal preferences and tolerances. Conventional approaches aim to achieve relatively uniform temperature conditions throughout a space, which implies that the thermal environment will likely be suboptimal for many of the individual occupants. Recent research efforts have integrated personal comfort models with heating, ventilation and air conditioning (HVAC) controls and have shown promising improvements by taking a highly individualistic approach to evaluating thermal comfort and adjusting HVAC operations accordingly. In this work, we aim to further advance occupant-centric controls by evaluating the benefits that could be gained by explicitly influencing and leveraging the development of non-uniform thermal conditions within a space. In particular, we consider the context of a multi-occupancy open office space shared by six occupants, where the thermal distribution patterns can be influenced by controlling the direction and flow rate of conditioned air being supplied through a central diffuser. Computational fluid dynamics was used to model and simulate thermal distribution patterns. Six probabilistic thermal comfort profiles were used to quantify the likelihood of each occupant being comfortable under the various control settings and location assignments. We analyzed three control strategies with incremental complexity, and collective comfort probability was shown to improve by 11%, 22%, and 30%, respectively. Our results also showed the potential energy-saving pathway by altering supply airflow direction instead of changing supply airflow rate to adjust thermal conditions in shared environments.
Highlights Collective comfort probability can be improved by leveraging thermal distribution patterns and personal comfort models. Occupants are subjected to different thermal conditions in multi-occupancy spaces. Thermal comfort of occupants should be assessed based on their micro-locations in shared spaces. Adjusting supply airflow direction can be used as an alternative strategy for adjusting thermal conditions. Thermal distribution patterns can be analyzed with CFD simulations.
Collective comfort optimization in multi-occupancy environments by leveraging personal comfort models and thermal distribution patterns
https://orcid.org/0000-0002-0561-359X
https://orcid.org/0000-0003-4734-0750
https://orcid.org/0000-0001-5888-4265
Abstract Buildings with shared spaces present a unique challenge for maintaining thermal comfort due to their dynamic occupancy patterns and the potential for wide variation in occupant thermal preferences and tolerances. Conventional approaches aim to achieve relatively uniform temperature conditions throughout a space, which implies that the thermal environment will likely be suboptimal for many of the individual occupants. Recent research efforts have integrated personal comfort models with heating, ventilation and air conditioning (HVAC) controls and have shown promising improvements by taking a highly individualistic approach to evaluating thermal comfort and adjusting HVAC operations accordingly. In this work, we aim to further advance occupant-centric controls by evaluating the benefits that could be gained by explicitly influencing and leveraging the development of non-uniform thermal conditions within a space. In particular, we consider the context of a multi-occupancy open office space shared by six occupants, where the thermal distribution patterns can be influenced by controlling the direction and flow rate of conditioned air being supplied through a central diffuser. Computational fluid dynamics was used to model and simulate thermal distribution patterns. Six probabilistic thermal comfort profiles were used to quantify the likelihood of each occupant being comfortable under the various control settings and location assignments. We analyzed three control strategies with incremental complexity, and collective comfort probability was shown to improve by 11%, 22%, and 30%, respectively. Our results also showed the potential energy-saving pathway by altering supply airflow direction instead of changing supply airflow rate to adjust thermal conditions in shared environments.
Highlights Collective comfort probability can be improved by leveraging thermal distribution patterns and personal comfort models. Occupants are subjected to different thermal conditions in multi-occupancy spaces. Thermal comfort of occupants should be assessed based on their micro-locations in shared spaces. Adjusting supply airflow direction can be used as an alternative strategy for adjusting thermal conditions. Thermal distribution patterns can be analyzed with CFD simulations.
Collective comfort optimization in multi-occupancy environments by leveraging personal comfort models and thermal distribution patterns
Topak, Fatih (author) / Pavlak, Gregory S. (author) / Pekeriçli, Mehmet Koray (author) / Wang, Julian (author) / Jazizadeh, Farrokh (author)
Building and Environment ; 239
2023-05-05
Article (Journal)
Electronic Resource
English
Personal thermal comfort models with wearable sensors
| Elsevier | 2019
A systematic review of personal thermal comfort models
| Elsevier | 2021
| British Library Online Contents | 2018