Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator
https://orcid.org/0000-0003-2746-4983
https://orcid.org/0000-0002-2231-1606
https://orcid.org/0000-0003-1886-9137
Abstract Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. The derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.
Highlights Representative residential occupancy schedules in US were identified via clustering. Over 90,000 smart thermostat operational data were used for analyses. The house type and location did not impact occupancy schedules, but day of the week. A tool that creates stochastic residential occupancy schedules was devised.
Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator
https://orcid.org/0000-0003-2746-4983
https://orcid.org/0000-0002-2231-1606
https://orcid.org/0000-0003-1886-9137
Abstract Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. The derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.
Highlights Representative residential occupancy schedules in US were identified via clustering. Over 90,000 smart thermostat operational data were used for analyses. The house type and location did not impact occupancy schedules, but day of the week. A tool that creates stochastic residential occupancy schedules was devised.
Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator
https://orcid.org/0000-0003-2746-4983
https://orcid.org/0000-0002-2231-1606
https://orcid.org/0000-0003-1886-9137
Abstract Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. The derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.
Highlights Representative residential occupancy schedules in US were identified via clustering. Over 90,000 smart thermostat operational data were used for analyses. The house type and location did not impact occupancy schedules, but day of the week. A tool that creates stochastic residential occupancy schedules was devised.
Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator
Jung, Wooyoung (Autor:in) / Wang, Zhe (Autor:in) / Hong, Tianzhen (Autor:in) / Jazizadeh, Farrokh (Autor:in)
Building and Environment ; 243
17.07.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Developing a residential occupancy schedule generator based on smart thermostat data
| Elsevier | 2024
Stochastic residential occupancy schedules based on the American Time-Use Survey
| Taylor & Francis Verlag | 2022