Data-driven Occupant Behavior Models
Data-Driven Occupant Behavior Model is a computational modeling approach that belongs to the class of occupant behavior models and is distinguished by its reliance on empirical data to infer behavioral patterns, typically through statistical or machine learning methods, without requiring explicit a priori assumptions about human decision-making processes.
17
Publications
634
Citations
91
Authors
42
Institutions
Publications per year
2017–2026
10
Parents
17
Martina Ferrando, Sibilla Ferroni, Martina Pelle et al. · Sustainable Cities and Society · 2022 · 56 citations · Full text
Engineering, Mechanical Engineering, Systems Engineering +15
1–5 of 17
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Norwegian University of Science and Technology, Norway
1 publications · 39 citations
1
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1
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1–5 of 91
42

Concordia University
Montreal, Canada
2 h-index
2 publications
53 citations

Pacific Northwest National Laboratory
Richland, United States
2 h-index
5 publications
143 citations
Universidade Federal de Santa Catarina
Florianópolis, Brazil
2 h-index
2 publications
53 citations

Carleton University
Ottawa, Canada
2 h-index
3 publications
178 citations
RWTH Aachen University
Aachen, Germany
2 h-index
5 publications
120 citations

Oak Ridge National Laboratory
Oak Ridge, United States
2 h-index
5 publications
161 citations
The University of Texas at Austin
Austin, United States
1 h-index
1 publications
14 citations
Dalian University of Technology
Dalian, China
1 h-index
2 publications
48 citations
1–8 of 42
3 publications · 147 citations
3
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2 publications · 50 citations
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2 publications · 96 citations
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1 publications · 82 citations
1
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1 publications · 33 citations
1
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