Dr. Tianzhen Hong is Senior Scientist with Building Technology and Urban Systems Division of LBNL. His research employs interdisciplinary approaches to explore technologies and human factors supporting the planning, design and operation of energy efficient, demand flexible, and climate resilient buildings across scales. He is an IBPSA Fellow and ASHRAE Fellow. He is a Highly Cited Researcher 2021 and 2022. He led energy software CityBES and CBES winning two R&D 100 Awards in 2019 and 2022.
This paper introduces a database of 34 field-measured building occupant behavior datasets collect... more This 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...
Research in occupant behaviour is now using a more elaborate framework of building occupant inter... more Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-NeedsActions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and I...
Author(s): Piette, M; Zarin Pass, R; Singh, R; Hong, T | Abstract: Currently over $300B is spent ... more Author(s): Piette, M; Zarin Pass, R; Singh, R; Hong, T | Abstract: Currently over $300B is spent in US city economies to pay for energy. Many US cities are taking leading roles in exploring and promoting activities to improve energy efficiency and reduce green house gas (GHG) emissions. This paper summarizes a series of interviews with several leading cities regarding their needs, methods and tools they are using to model energy use and evaluate policies to reduce GHG. We also present a review of several analysis tools evaluated and used to explore urban scale design scenarios for two new major developments in the San Francisco area. We found that cities face great challenges managing data on their building stock, obtaining energy use data, and evaluating the different tools that are available to them. There is a need for better data management systems that allow tools to be more interoperable. The wide variety and features of today’s tools, and the fact that many of them are not op...
This paper introduces a database of 34 field-measured building occupant behavior datasets collect... more This 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...
Research in occupant behaviour is now using a more elaborate framework of building occupant inter... more Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-NeedsActions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and I...
Author(s): Piette, M; Zarin Pass, R; Singh, R; Hong, T | Abstract: Currently over $300B is spent ... more Author(s): Piette, M; Zarin Pass, R; Singh, R; Hong, T | Abstract: Currently over $300B is spent in US city economies to pay for energy. Many US cities are taking leading roles in exploring and promoting activities to improve energy efficiency and reduce green house gas (GHG) emissions. This paper summarizes a series of interviews with several leading cities regarding their needs, methods and tools they are using to model energy use and evaluate policies to reduce GHG. We also present a review of several analysis tools evaluated and used to explore urban scale design scenarios for two new major developments in the San Francisco area. We found that cities face great challenges managing data on their building stock, obtaining energy use data, and evaluating the different tools that are available to them. There is a need for better data management systems that allow tools to be more interoperable. The wide variety and features of today’s tools, and the fact that many of them are not op...
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