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

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  • Papadopoulos, Sokratis
  • Bonczak, Bartosz
  • Kontokosta, Constantine E.

Abstract

In recent years, many cities have adopted energy disclosure policies to better understand how energy is consumed in the urban built environment and how energy use and carbon emissions can be reduced. The diffusion of such policies has generated large-scale streams of building energy data, creating new opportunities to develop the fundamental science of urban energy dynamics. Nevertheless, there is limited research that rigorously analyzes building energy performance patterns over time. This paper provides a comprehensive framework to analyze building energy time series data and identify buildings with similar temporal energy performance patterns. We use data from approximately 15,000 properties in New York City, covering a six-year reporting period from 2011 to 2016. After pre-processing and merging the data for each constituent year, we use an unsupervised learning algorithm to optimally cluster the energy time series and statistical tests and supervised learning methods to infer how building characteristics vary between clusters. Our results show that energy reductions in New York City are mainly driven by its commercial building stock, with larger, newer, and higher-value buildings demonstrating the largest improvements in energy intensity over the study period. Moreover, voluntary energy conservation schemes are found to be more effective in boosting energy performance of commercial properties, compared to residential buildings. Our results suggest two distinct temporal patterns of energy performance for commercial and residential buildings, characterized by energy use reductions and increases. This finding highlights the differential response to energy reporting and disclosure, and presents a more complex picture of energy use dynamics over time when compared to previous studies. In order to realize significant energy use improvements over time and reach energy and carbon reduction goals, cities need to design and implement comprehensive energy policy frameworks, bringing together information transparency and reporting with targeted mandates and incentives.

Suggested Citation

  • Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    • Handle: RePEc:eee:appene:v:221:y:2018:i:c:p:576-586
    DOI: 10.1016/j.apenergy.2018.03.079
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    References listed on IDEAS

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    1. Constantine Kontokosta, 2015. "A Market-Specific Methodology for a Commercial Building Energy Performance Index," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 288-316, August.
    2. Palmer, Karen & Walls, Margaret, 2015. "Can Benchmarking and Disclosure Laws Provide Incentives for Energy Efficiency Improvements in Buildings?," RFF Working Paper Series dp-15-09, Resources for the Future.
    3. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    4. Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
    5. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    6. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    7. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    8. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    9. Reina, Vincent J. & Kontokosta, Constantine, 2017. "Low hanging fruit? Regulations and energy efficiency in subsidized multifamily housing," Energy Policy, Elsevier, vol. 106(C), pages 505-513.
    10. Gaitani, N. & Lehmann, C. & Santamouris, M. & Mihalakakou, G. & Patargias, P., 2010. "Using principal component and cluster analysis in the heating evaluation of the school building sector," Applied Energy, Elsevier, vol. 87(6), pages 2079-2086, June.
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    Cited by:

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    5. ChungYeon Won & SangTae No & Qamar Alhadidi, 2019. "Factors Affecting Energy Performance of Large-Scale Office Buildings: Analysis of Benchmarking Data from New York City and Chicago," Energies, MDPI, vol. 12(24), pages 1-17, December.
    6. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    7. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    10. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
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    14. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    15. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    16. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    17. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
    18. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    19. Chunyan Wang & Hanying Jiang & Hao Wu & Yi Liu & Siyue Guo & Ming Xu, 2023. "Scaling in urban building energy use and its influencing factors," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1076-1088, August.
    20. Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).

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