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

Authors

91

  1. DY
    Da Yan

    Tsinghua University, China

    2 publications · 156 citations

    2

    h-index

  2. NS
    Nikhil Singh Yaduvanshi

    Concordia University, Canada

    1 publications · 14 citations

    1

    h-index

  3. ER
    Edwin R. Galea

    University of Greenwich, United Kingdom

    1 publications · 15 citations

    1

    h-index

  4. SC
    Salvatore Carlucci

    Norwegian University of Science and Technology, Norway

    1 publications · 39 citations

    1

    h-index

  5. ZL
    Zhiwen Luo

    Huashan Hospital, China

    1 publications · 24 citations

    1

    h-index

1–5 of 91

Venues

  1. Energy and Buildings

    3 publications · 147 citations

    3

    h-index

  2. Energy

    2 publications · 50 citations

    2

    h-index

  3. Sustainable Cities and Society

    2 publications · 96 citations

    2

    h-index

  4. Journal of Building Performance Simulation

    1 publications · 82 citations

    1

    h-index

  5. Renewable and Sustainable Energy Reviews

    1 publications · 33 citations

    1

    h-index

1–5