Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Occupancy and occupant activity drivers of energy consumption in residential buildings
Highlights Occupancy time-series data are clustered to capture the diversity in the daily occupancy schedule of apartments. The regular high- and low-energy consumption hours are identified using change point detection. Energy-saving and load-shifting feedbacks are provided based on occupant activity drivers. The developed framework can be extended to different households with different presence and activity routines.
Abstract There has been an increasing interest in addressing and discovering the factors influencing the households’ load profiles instead of their end-use energy demand. The rationale behind this tendency is to provide households with load shifting recommendations and flatten the load profiles by making use of the knowledge obtained from these temporal and contextual determinants. Methodologies connecting households’ activities and presence to load profiles are often under-investigated, and the flexibility in the presence and activity routines of households throughout a long period is ignored. In this study, a data-driven framework is developed to extract households’ daily occupancy patterns throughout a year, determine the regular high- and low-energy consumption periods, and discover influencing activity factors of energy consumption within the obtained periods. The purpose of this study is to provide households with customized load-shifting and energy-saving suggestions based on their specific traits and routines. The results suggest that the distribution of occupancy patterns between seasons and weekdays varies considerably among different households. It is further recognized that days with similar occupancy patterns can have nearly similar peak timings in different apartments. The developed framework is generic and can be generalized to different households with different presence and activity routines.
Occupancy and occupant activity drivers of energy consumption in residential buildings
Highlights Occupancy time-series data are clustered to capture the diversity in the daily occupancy schedule of apartments. The regular high- and low-energy consumption hours are identified using change point detection. Energy-saving and load-shifting feedbacks are provided based on occupant activity drivers. The developed framework can be extended to different households with different presence and activity routines.
Abstract There has been an increasing interest in addressing and discovering the factors influencing the households’ load profiles instead of their end-use energy demand. The rationale behind this tendency is to provide households with load shifting recommendations and flatten the load profiles by making use of the knowledge obtained from these temporal and contextual determinants. Methodologies connecting households’ activities and presence to load profiles are often under-investigated, and the flexibility in the presence and activity routines of households throughout a long period is ignored. In this study, a data-driven framework is developed to extract households’ daily occupancy patterns throughout a year, determine the regular high- and low-energy consumption periods, and discover influencing activity factors of energy consumption within the obtained periods. The purpose of this study is to provide households with customized load-shifting and energy-saving suggestions based on their specific traits and routines. The results suggest that the distribution of occupancy patterns between seasons and weekdays varies considerably among different households. It is further recognized that days with similar occupancy patterns can have nearly similar peak timings in different apartments. The developed framework is generic and can be generalized to different households with different presence and activity routines.
Occupancy and occupant activity drivers of energy consumption in residential buildings
Akbari, Saba (Autor:in) / Haghighat, Fariborz (Autor:in)
Energy and Buildings ; 250
22.07.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Time-series analysis , Occupancy , Load profile , Load shifting , Data-driven framework , Apt , Apartment , BATH , Bathroom , BED , Bedroom , BEMS , Building Energy Management Systems , CPD , Change Point Detection , CVI , Cluster Validation Index , DTW , Dynamic Time Warping , HEMS , Home Energy Management System , HVAC , Heating, Ventilating, and Air-Conditioning , KIT , Kitchen , LIGHT , Lighting , LIV , Living room , OLS , Ordinary Least Squares , OTH , Other , RSS , Residual Sum of Squares , SBD , Shape-Based Distance , SIC , Schwarz Information Criterion , TUS , Time-Use Survey , VIF , Variance-Inflation factor