The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
A data-driven predictive model of city-scale energy use in buildings
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In the citing paper
“Based on energy data disclosed by residents of New York City, Kontokosta and colleagues used various ML methods to predict the energy use of the city's 1.1 million buildings [448], analyzed the effect of energy disclosure on the demand [610], and developed a system for ranking buildings based on energy efficiency [611].”
From Tackling climate change with machine learning (2023) · cites this paper as [448]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
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Prasad, A. M., Iverson, L. R., & Liaw, A. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 1,987
A review on the prediction of building energy consumption
Zhao, H., & Magoulès, F. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.049 · Cited by 1,947
Analysis of a Random Forests Model
Biau, G. (2010) · Journal of machine learning research · doi:10.5555/2503308.2343682 · Cited by 1,690
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Research works printed but not linked: 1
- Hastie, 2009, vol. 2
Cited by
Sorted by citations
Ten questions on urban building energy modeling
Hong, T., Chen, Y., Luo, X., Luo, N., & Lee, S. H. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106508 · Cited by 587
Chen, Y., Hong, T., & Piette, M. A. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.128 · Cited by 408
Electricity load forecasting: a systematic review
Nti, I. K., Teimeh, M., Nyarko‐Boateng, O., & Adekoya, A. F. (2020) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-020-00021-8 · Cited by 400
Pan, Y., Zhu, M., Lv, Y. A., Yang, Y., Liang, Y., Yin, R., Yang, Y., Jia, X., Wang, X., Zeng, F., et al. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100135 · Cited by 371
How can artificial intelligence impact sustainability: A systematic literature review
Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.134120 · Cited by 369
Metadata
- Authors
- Kontokosta, C. E., & Tull, C.
- Affiliations
- New York University (US)
- Corresponding author
- Kontokosta, Constantine E.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 197
- Pages
- 303-317
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2605391457
- Cited by
- 288
- References
- 42
- Citations by year
- 2026: 35 · 2025: 38 · 2024: 25 · 2023: 30 · 2022: 34 · 2021: 35 · 2020: 33 · 2019: 24 · 2018: 27 · 2017: 7
- FWCI
- 23.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 295 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Applied Energy (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (1653772)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Physical Sciences › Engineering › Building and Construction › Sustainable Building Design and Assessment
- Keywords
- building energy benchmarking, electricity consumption, natural gas consumption, energy intensity, building age, building height, office buildings, residential buildings, New York City, urban energy policy
About these checks
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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