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A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings

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  • Ali, Usman
  • Shamsi, Mohammad Haris
  • Bohacek, Mark
  • Hoare, Cathal
  • Purcell, Karl
  • Mangina, Eleni
  • O’Donnell, James

Abstract

Urban planners face significant challenges when identifying building energy efficiency opportunities and developing strategies to achieve efficient and sustainable urban environments. A possible scalable solution to tackle this problem is through the analysis of building stock databases. Such databases can support and assist with building energy benchmarking and potential retrofit performance analysis. However, developing a building stock database is a time-intensive modeling procedure that requires extensive data (both geometric and non-geometric). Furthermore, the available data for developing a building database is sparse, inconsistent, diverse and heterogeneous in nature. The main aim of this study is to develop a generic methodology to optimize urban scale energy retrofit decisions for residential buildings using data-driven approaches. Furthermore, data-driven approaches identify the key features influencing building energy performance. The proposed methodology formulates retrofit solutions and identifies optimal features for the residential building stock of Dublin. Results signify the importance of data-driven retrofit modeling as the feature selection process reduces the number of features in Dublin’s building stock database from 203 to 56 with a building rating prediction accuracy of 86%. Amongst the 56 features, 16 are identified to be recommended as retrofit measures (such as fabric renovation values and heating system upgrade features) associated with each energy-efficiency rating. Urban planners and energy policymakers could use this methodology to optimize large-scale retrofit implementation, particularly at an urban scale with limited resources. Furthermore, stakeholders at the local authority level can estimate the required retrofit investment costs, emission reductions and energy savings using the target retrofit features of energy-efficiency ratings.

Suggested Citation

  • Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920303731
    DOI: 10.1016/j.apenergy.2020.114861
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    4. Ahmed, Wahhaj & Asif, Muhammad, 2021. "A critical review of energy retrofitting trends in residential buildings with particular focus on the GCC countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    6. 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).
    7. Riccardo Camboni & Alberto Corsini & Raffaele Miniaci & Paola Valbonesi, 2023. "CO2 emissions reduction from residential buildings: cost estimate and policy design," "Marco Fanno" Working Papers 0304, Dipartimento di Scienze Economiche "Marco Fanno".
    8. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    9. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    10. Yupei Lai & Yutong Li & Xinyi Feng & Tao Ma, 2022. "Green retrofit of existing residential buildings in China: An investigation on residents’ perceptions," Energy & Environment, , vol. 33(2), pages 332-353, March.
    11. Gabriele Battista & Emanuele de Lieto Vollaro & Andrea Vallati & Roberto de Lieto Vollaro, 2023. "Technical–Financial Feasibility Study of a Micro-Cogeneration System in the Buildings in Italy," Energies, MDPI, vol. 16(14), pages 1-15, July.
    12. Hanan S. S. Ibrahim & Ahmed Z. Khan & Yehya Serag & Shady Attia, 2021. "Towards Nearly-Zero Energy in Heritage Residential Buildings Retrofitting in Hot, Dry Climates," Sustainability, MDPI, vol. 13(24), pages 1-36, December.
    13. Duan, Haiyan & Chen, Siyan & Song, Junnian, 2022. "Characterizing regional building energy consumption under joint climatic and socioeconomic impacts," Energy, Elsevier, vol. 245(C).
    14. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    15. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

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