Cited by 294Building Energy and Comfort OptimizationSmart Grid Energy ManagementData Mining and Machine Learning Applications

Occupancy schedules learning process through a data mining framework

Open access
D’Oca, Simona, Hong, Tianzhen·2015·Energy and Buildings

Integrity signals

Registry

Crossref · 10.1016/j.enbuild.2014.11.065

Cites

31 of 31 references verified0 citation errors · 0 not checkable

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Claims the paper makes

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Claims other papers make about this one

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  • Occupancy detection represents an opportunity for ML algorithms, ranging from decision trees to deep neural networks using various inputs.

    “Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [193,882] to deep neural networks [896] that take input from occupancy sensors [193], WiFi signals [896,897], or appliance power consumption data [882].”

    From Tackling climate change with machine learning (2023) · cites this paper as [193]

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Authors
D’Oca, S., & Hong, T.
Affiliations
Lawrence Berkeley National Laboratory (US)
Corresponding author
Hong, Tianzhen
Venue
Energy and Buildings
Publisher
Elsevier BV
Published
2015-02
Volume
88
Pages
395-408
ISSN
0378-7788 (ISSN-L)
OpenAlex
W1981885501
Cited by
294
References
33
Citations by year
2026: 12 · 2025: 17 · 2024: 17 · 2023: 26 · 2022: 27 · 2021: 33 · 2020: 32 · 2019: 25 · 2018: 36 · 2017: 31 · 2016: 26 · 2015: 12
FWCI
31.81
Type
journal article
Language
en
Open access
Locations
  • Energy and Buildings (acceptedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • U.S. Department of Energy (DE-AC02-05CH11231)
  • U.S.-China Clean Energy Research Center
Topics
  • Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
building occupancy, occupancy schedules, office buildings, occupancy patterns, building energy simulation, decision tree, rule induction, clustering, occupant behavior, human-building interaction

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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