Occupant-centric localized heating/cooling is crucial for advancing building carbon neutrality and enhancing habitation quality.This strategy hinges on achieving precise match between thermal supply and individual dem...Occupant-centric localized heating/cooling is crucial for advancing building carbon neutrality and enhancing habitation quality.This strategy hinges on achieving precise match between thermal supply and individual demand across both temporal and spatial scales,thereby minimizing unnecessary energy consumption.However,current research mainly relies on room-scale analyses that overlook fine-grained behavioral variabilities and personalized spatial preferences,constraining the development of refined environmental control systems.To address this gap,this study presents an occupant-centric method for indoor occupancy pattern analysis,introducing a Present Demand-Next Demand segment-based modeling framework that incorporates migration pathways and behavioral rhythms.It enhances the accuracy of behavioral pattern reconstruction and enables responsive,high-resolution environmental control.The framework supports the extraction of individual-scale occupancy patterns,facilitating dynamic and adaptive heating/cooling strategies.On this basis,the individual occupancy patterns of a three-person household was analyzed with field-tested positioning data.Results show that Resident Zones(RZs)account for over 85% of dwelling time while occupying only a small spatial fraction,indicating energy-saving potential through localized regulation.Behavioral analysis further reveals that different occupants exhibit distinct spatial preferences with strong connectivity between preferred zones,and that fixed transfer tendencies occur at specific times,suggesting opportunities for personalized control strategies.Moreover,different spatial clustering methods demonstrated distinct strengths under varying activity intensities,highlighting their complementarity for individual-scale behavioral analysis.Overall,this research provides support for advancing personalized environmental control,offering actionable insights for demand-responsive systems and performance-based building simulations.展开更多
基金supported by the National Natural Science Foundation of China(Grant 52478100,52425801,52130803,52408102)the Natural Science Foundation of Sichuan Province(2024NSFSC0916)+1 种基金the China Postdoctoral Science Foundation(2023M732479)Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows(BX202218).
文摘Occupant-centric localized heating/cooling is crucial for advancing building carbon neutrality and enhancing habitation quality.This strategy hinges on achieving precise match between thermal supply and individual demand across both temporal and spatial scales,thereby minimizing unnecessary energy consumption.However,current research mainly relies on room-scale analyses that overlook fine-grained behavioral variabilities and personalized spatial preferences,constraining the development of refined environmental control systems.To address this gap,this study presents an occupant-centric method for indoor occupancy pattern analysis,introducing a Present Demand-Next Demand segment-based modeling framework that incorporates migration pathways and behavioral rhythms.It enhances the accuracy of behavioral pattern reconstruction and enables responsive,high-resolution environmental control.The framework supports the extraction of individual-scale occupancy patterns,facilitating dynamic and adaptive heating/cooling strategies.On this basis,the individual occupancy patterns of a three-person household was analyzed with field-tested positioning data.Results show that Resident Zones(RZs)account for over 85% of dwelling time while occupying only a small spatial fraction,indicating energy-saving potential through localized regulation.Behavioral analysis further reveals that different occupants exhibit distinct spatial preferences with strong connectivity between preferred zones,and that fixed transfer tendencies occur at specific times,suggesting opportunities for personalized control strategies.Moreover,different spatial clustering methods demonstrated distinct strengths under varying activity intensities,highlighting their complementarity for individual-scale behavioral analysis.Overall,this research provides support for advancing personalized environmental control,offering actionable insights for demand-responsive systems and performance-based building simulations.
