The DOI matches its Crossref record; 31 of the 33 references it cites were found in a registry or an index.
Occupancy schedules learning process through a data mining framework
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Integrity signals
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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 [193]
Citations
Cites
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Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Advances in knowledge discovery and data mining
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90007-3 · Cited by 2,996
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Fast discovery of association rules
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Smith, A. E. (2002) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(02)00058-1 · Cited by 1,711
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Research works printed but not linked: 1
- RapidMiner Studio, V 5.3, Cambridge, MA 02138, USA 〈http://rapid-i.com/content/view/181/190/〉.
Cited by
Sorted by citations
Advances in research and applications of energy-related occupant behavior in buildings
Hong, T., Taylor-Lange, S. C., D’Oca, S., Yan, D., & Corgnati, S. P. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.11.052 · Cited by 543
Self-citationRethinking the role of occupant behavior in building energy performance: A review
Zhang, Y., Bai, X., Mills, F. P., & Pezzey, J. C. V. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.05.017 · Cited by 478
The human dimensions of energy use in buildings: A review
D’Oca, S., Hong, T., & Langevin, J. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.019 · Cited by 394
Self-citationA review of smart building sensing system for better indoor environment control
Dong, B., Prakash, V., Feng, F., & O’Neill, Z. D. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.06.025 · Cited by 369
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Metadata
- 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
- bronze
- doi.org
- Licence
- 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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