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Estimating building occupancy: a machine learning system for day, night, and episodic events.

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Title: Estimating building occupancy: a machine learning system for day, night, and episodic events.
Authors: Urban, Marie1 (AUTHOR) urbanml@ornl.gov, Stewart, Robert1 (AUTHOR), Basford, Scott1 (AUTHOR), Palmer, Zachary1 (AUTHOR), Kaufman, Jason1 (AUTHOR)
Source: Natural Hazards. Mar2023, Vol. 116 Issue 2, p2417-2436. 20p.
Subject Terms: *Construction cost estimates, *Machine learning, *Instructional systems, *Human activity recognition, *Structural dynamics, *Data harmonization
Company/Entity: Oak Ridge National Laboratory
Abstract: Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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PubType: Academic Journal
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  Data: Estimating building occupancy: a machine learning system for day, night, and episodic events.
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  Data: <searchLink fieldCode="JN" term="%22Natural+Hazards%22">Natural Hazards</searchLink>. Mar2023, Vol. 116 Issue 2, p2417-2436. 20p.
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  Data: *<searchLink fieldCode="DE" term="%22Construction+cost+estimates%22">Construction cost estimates</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Human+activity+recognition%22">Human activity recognition</searchLink><br />*<searchLink fieldCode="DE" term="%22Structural+dynamics%22">Structural dynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+harmonization%22">Data harmonization</searchLink>
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  Data: Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=162853212
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        Value: 10.1007/s11069-022-05772-3
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        Text: English
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      – SubjectFull: Instructional systems
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      – SubjectFull: Human activity recognition
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      – SubjectFull: Structural dynamics
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      – SubjectFull: Data harmonization
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      – SubjectFull: Oak Ridge National Laboratory
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            NameFull: Urban, Marie
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            NameFull: Basford, Scott
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            NameFull: Palmer, Zachary
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              Text: Mar2023
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