Grading buildings on energy performance using city benchmarking data
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [611]
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 9,622 · see citing works
A review on buildings energy consumption information
Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2007.03.007 · Cited by 6,649 · see citing works
Social norms and energy conservation
Allcott, H. (2011) · Journal of Public Economics · doi:10.1016/j.jpubeco.2011.03.003 · Cited by 2,715 · see citing works
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286 · see citing works
Zhang, C., & Ma, Y. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7 · Cited by 2,238 · see citing works
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 · see citing works
de Wilde, P. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.02.009 · Cited by 1,048 · see citing works
Allcott, H., & Mullainathan, S. (2010) · Science · doi:10.1126/science.1180775 · Cited by 852 · see citing works
A review of data-driven approaches for prediction and classification of building energy consumption
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., & Zhao, X. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.09.108 · Cited by 811 · see citing works
A short-term building cooling load prediction method using deep learning algorithms
Fan, C., Xiao, F., & Zhao, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.03.064 · Cited by 695 · see citing works
Residential energy use and conservation: Economics and demographics
Brounen, D., Kok, N., & Quigley, J. M. (2012) · European Economic Review · doi:10.1016/j.euroecorev.2012.02.007 · Cited by 630 · see citing works
Deep learning for estimating building energy consumption
Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2016.02.005 · Cited by 627 · see citing works
Buildings and Climate Change: Summary for Decision-Makers
Huovila, P., Ala-Juusela, M., Melchert, L., et al. (2009) · Cited by 618 · see citing works
Kavousian, A., Rajagopal, R., & Fischer, M. A. (2013) · Energy · doi:10.1016/j.energy.2013.03.086 · Cited by 602 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierPreprint
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04938 · Cited by 587 · see citing works
Machine learning approaches for estimating commercial building energy consumption
Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., & Pendyala, R. M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.09.060 · Cited by 482 · see citing works
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Li, C., Ding, Z., Zhao, D., Yi, J., & Zhang, G. (2017) · Energies · doi:10.3390/en10101525 · Cited by 300 · see citing works
Fuerst, F., McAllister, P. M., Nanda, A., & Wyatt, P. (2015) · Energy Economics · doi:10.1016/j.eneco.2014.12.012 · Cited by 298 · see citing works
A data-driven predictive model of city-scale energy use in buildings
Kontokosta, C. E., & Tull, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.005 · Cited by 288 · see citing works
Self-citationvan Dronkelaar, C., Dowson, M., Spataru, C., et al. (2016) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2015.00017 · Cited by 265 · see citing works
Rating the energy performance of buildings
Olofsson, T., Meier, A., & Lamberts, R. (2004) · University of North Texas Digital Library (University of North Texas) · Cited by 232 · see citing works
Saving power to conserve your reputation? The effectiveness of private versus public information
Delmas, M. A., & Lessem, N. (2014) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2013.12.009 · Cited by 227 · see citing works
Using intelligent clustering techniques to classify the energy performance of school buildings
Santamouris, M., Mihalakakou, G. K., Patargias, P., et al. (2006) · Energy and Buildings · doi:10.1016/j.enbuild.2006.04.018 · Cited by 209 · see citing works
Energy performance ratings and house prices in Wales: An empirical study
Fuerst, F., McAllister, P., Nanda, A., et al. (2016) · Energy Policy · doi:10.1016/j.enpol.2016.01.024 · Cited by 182 · see citing works
Hsu, D. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.126 · Cited by 174 · see citing works
Gao, X., & Malkawi, A. M. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.08.030 · Cited by 172 · see citing works
Benchmarking energy use and greenhouse gas emissions in Singapore’s hotel industry
Xuchao, W., Rajagopalan, P., & Eang, L. S. (2010) · Energy Policy · doi:10.1016/j.enpol.2010.04.006 · Cited by 171 · see citing works
Evaluating energy performance in non-domestic buildings: A review
Borgstein, E. H., Lamberts, R., & Hensen, J. J. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.07.018 · Cited by 165 · see citing works
Evaluation of tree-based ensemble learning algorithms for building energy performance estimation
Papadopoulos, S., Azar, E., Woon, W. L., & Kontokosta, C. E. (2017) · Journal of Building Performance Simulation · doi:10.1080/19401493.2017.1354919 · Cited by 162 · see citing works
Self-citationIs energy efficiency capitalized into home prices? Evidence from three U.S. cities
Walls, M. A., Gerarden, T., Palmer, K., & Bak, X. F. (2016) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2016.11.006 · Cited by 128 · see citing works
Wyatt, P. (2013) · Energy Policy · doi:10.1016/j.enpol.2013.05.037 · Cited by 126 · see citing works
Hsu, D. (2015) · Energy · doi:10.1016/j.energy.2015.02.008 · Cited by 99 · see citing works
Zheng, Y., Roth, J., & Jain, R. K. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.12.040 · Cited by 92 · see citing works
Development of surrogate models using artificial neural network for building shell energy labelling
Melo, A. P., Cóstola, D. D., Lamberts, R., et al. (2014) · Energy Policy · doi:10.1016/j.enpol.2014.02.001 · Cited by 85 · see citing works
Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings
Chung, W. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.01.061 · Cited by 81 · see citing works
Pattern recognition in building energy performance over time using energy benchmarking data
Papadopoulos, S., Bonczak, B., & Kontokosta, C. E. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.079 · Cited by 76 · see citing works
Self-citationYalcintas, M. (2006) · International Journal of Energy Research · doi:10.1002/er.1212 · Cited by 66 · see citing works
Palmer, K., & Walls, M. A. (2016) · Energy Efficiency · doi:10.1007/s12053-016-9480-5 · Cited by 66 · see citing works
Wong, M. R., McKelvey, W. F., Ito, K., et al. (2015) · American Journal of Public Health · doi:10.2105/ajph.2014.302404 · Cited by 62 · see citing works
A Market-Specific Methodology for a Commercial Building Energy Performance Index
Kontokosta, C. E. (2014) · The Journal of Real Estate Finance and Economics · doi:10.1007/s11146-014-9481-0 · Cited by 60 · see citing works
Self-citationEstimating energy savings from benchmarking policies in New York City
Meng, T., Hsu, D. D., & Han, A. T. (2017) · Energy · doi:10.1016/j.energy.2017.05.148 · Cited by 60 · see citing works
Coarse Grades: Informing the Public by Withholding Information
Harbaugh, R., & Rasmusen, E. (2018) · American Economic Journal Microeconomics · doi:10.1257/mic.20130078 · Cited by 57 · see citing works
Energy disclosure, market behavior, and the building data ecosystem
Kontokosta, C. E. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12163 · Cited by 56 · see citing works
Self-citationKhayatian, F., Sarto, L., & Dall'O', G. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.08.237 · Cited by 40 · see citing works
Improving energy benchmarking with self-reported data
Hsu, D. (2014) · Building Research & Information · doi:10.1080/09613218.2014.887612 · Cited by 39 · see citing works
Papadopoulos, S., Bonczak, B. J., & Kontokosta, C. E. (2017) · International Conference on Sustainable Infrastructure (ICSI) · doi:10.1061/9780784481219.030 · Cited by 7 · see citing works
Self-citationDevelopments in Local Energy Efficiency Policy: a Review of Recent Progress and Research
Ribeiro, D. (2018) · Current Sustainable/Renewable Energy Reports · doi:10.1007/s40518-018-0105-9 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
- Authors
- Papadopoulos, S., & Kontokosta, C. E.
- Affiliations
- New York University (US)
- Corresponding author
- Kontokosta, Constantine E.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 233-234
- Pages
- 244-253
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2897784558
- Cited by
- 177
- References
- 49
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 28 · 2023: 17 · 2022: 32 · 2021: 20 · 2020: 22 · 2019: 10 · 2018: 1
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- 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
- energy benchmarking, energy rating, residential buildings, New York City, carbon emission reduction, building energy consumption
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review