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A data-driven predictive model of city-scale energy use in buildings
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“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]
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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
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