A Global Building Occupant Behavior Database
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Published:2022-06-28
Issue:1
Volume:9
Page:
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ISSN:2052-4463
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Container-title:Scientific Data
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language:en
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Short-container-title:Sci Data
Author:
Dong Bing, Liu YapanORCID, Mu Wei, Jiang Zixin, Pandey Pratik, Hong TianzhenORCID, Olesen Bjarne, Lawrence Thomas, O’Neil Zheng, Andrews Clinton, Azar ElieORCID, Bandurski Karol, Bardhan RonitaORCID, Bavaresco Mateus, Berger Christiane, Burry Jane, Carlucci SalvatoreORCID, Chvatal KarinORCID, De Simone MarilenaORCID, Erba Silvia, Gao Nan, Graham Lindsay T., Grassi Camila, Jain Rishee, Kumar SanjayORCID, Kjærgaard MikkelORCID, Korsavi SepidehORCID, Langevin JaredORCID, Li Zhengrong, Lipczynska AleksandraORCID, Mahdavi Ardeshir, Malik Jeetika, Marschall Max, Nagy ZoltanORCID, Neves Leticia, O’Brien William, Pan Song, Park June Young, Pigliautile Ilaria, Piselli Cristina, Pisello Anna Laura, Rafsanjani Hamed Nabizadeh, Rupp Ricardo ForgiariniORCID, Salim Flora, Schiavon StefanoORCID, Schwee JensORCID, Sonta Andrew, Touchie Marianne, Wagner Andreas, Walsh Sinead, Wang Zhe, Webber David M., Yan DaORCID, Zangheri Paolo, Zhang Jingsi, Zhou Xiang, Zhou Xin
Abstract
AbstractThis paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.
Funder
American Society of Heating, Refrigerating and Air-Conditioning Engineers
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
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