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Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining
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“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 [882]
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Metadata
- Authors
- Zhao, J., Lasternas, B., Lam, K. P., Yun, R., & Loftness, V.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Zhao, Jie
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 82
- Pages
- 341-355
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2071158874
- Cited by
- 293
- References
- 66
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 29 · 2023: 13 · 2022: 26 · 2021: 42 · 2020: 33 · 2019: 27 · 2018: 30 · 2017: 32 · 2016: 24 · 2015: 20 · 2014: 1
- FWCI
- 38.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy and Buildings (publishedVersion)
- KiltHub Repository (submittedVersion)
- Funders
- National Science Foundation (NSF)
- Efficient Buildings Hub Consortium
- 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 › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- occupant behavior, data mining, building energy simulation, HVAC energy consumption, indoor environment, sustainable building design, EnergyPlus, climate zone effects
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