Abstract
Thermal comfort is one of the main driving factors in defining the operational settings of HVAC systems, and it greatly impacts energy efficiency in buildings. Lack of information about human related variables results in using unrepresentative operational settings, which in turn could bring about low efficiency in HVAC operations. In this paper, the implementation and evaluation of a framework that integrates building occupants’ personalized thermal profiles into the HVAC control logic is presented. The framework enables occupants to communicate their preferences for indoor thermal conditions through a user interface, leveraging a participatory sensing approach. The framework learns occupants’ comfort profiles, using a fuzzy predictive model, and controls the HVAC system using a complementary control strategy, which enables the framework to be implemented in existing centrally controlled HVAC systems with minimum intrusion. Evaluation of the framework in a real building setting showed user comfort improvement. Moreover, the results showed a 39% reduction in daily average airflow when the HVAC system conditions the rooms at occupants’ desired temperatures. Airflow is proportional to the energy consumption of HVAC system components. Consequently, the implementation of the framework shows improvements in the efficiency of the HVAC system's performance for centrally controlled office buildings.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
