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Cited by 76Building Energy and Comfort OptimizationBuilding energy efficiency and sustainabilitySustainable Building Design and Assessment

Pattern recognition in building energy performance over time using energy benchmarking data

Open access
Papadopoulos, Sokratis, Bonczak, Bartosz, Kontokosta, Constantine E.·2018·Applied Energy
DOI 10.1016/j.apenergy.2018.03.079
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Crossref · 10.1016/j.apenergy.2018.03.079

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  • Kontokosta and colleagues used ML to predict energy use of New York City's 1.1 million buildings, analyzed energy disclosure effect, and developed a ranking system.

    “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 [610]

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References 44 · Cited by 76

Authors
Papadopoulos, S., Bonczak, B., & Kontokosta, C. E.
Affiliations
New York University (US)
Corresponding author
Kontokosta, Constantine E.
ORCID
Venue
Applied Energy
Publisher
Elsevier BV
Published
2018-07
Volume
221
Pages
576-586
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2018.03.079
OpenAlex
W2800755917
Cited by
76
References
44
Citations by year
2026: 6 · 2025: 7 · 2024: 4 · 2023: 9 · 2022: 12 · 2021: 12 · 2020: 11 · 2019: 7 · 2018: 8
FWCI
7.61
Type
journal article
Language
en
Open access
Licence
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 performance, energy benchmarking, commercial buildings, residential buildings, energy intensity, New York City, energy policy

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