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Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits

Author

Listed:
  • Jenny von Platten

    (Division of Built Environment, RISE Research Institutes of Sweden, Sven Hultins plats 5, 412 58 Gothenburg, Sweden
    Department of Building and Environmental Technology, Faculty of Engineering, Lund University, Ole Römers väg 1, Box 118, 221 00 Lund, Sweden)

  • Claes Sandels

    (Division of Safety and Transport, RISE Research Institutes of Sweden, Sven Hultins plats 5, 412 58 Gothenburg, Sweden)

  • Kajsa Jörgensson

    (Department of Energy Sciences, Faculty of Engineering, Lund University, Ole Römers väg 1, Box 118, 221 00 Lund, Sweden)

  • Viktor Karlsson

    (Department of Energy Sciences, Faculty of Engineering, Lund University, Ole Römers väg 1, Box 118, 221 00 Lund, Sweden)

  • Mikael Mangold

    (Division of Built Environment, RISE Research Institutes of Sweden, Sven Hultins plats 5, 412 58 Gothenburg, Sweden)

  • Kristina Mjörnell

    (Department of Building and Environmental Technology, Faculty of Engineering, Lund University, Ole Römers väg 1, Box 118, 221 00 Lund, Sweden
    Sustainable Cities and Communities, RISE Research Institutes of Sweden, Sven Hultins plats 5, 412 58 Gothenburg, Sweden)

Abstract

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.

Suggested Citation

  • Jenny von Platten & Claes Sandels & Kajsa Jörgensson & Viktor Karlsson & Mikael Mangold & Kristina Mjörnell, 2020. "Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits," Energies, MDPI, vol. 13(10), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2574-:d:360147
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    References listed on IDEAS

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    1. Pasichnyi, Oleksii & Wallin, Jörgen & Levihn, Fabian & Shahrokni, Hossein & Kordas, Olga, 2019. "Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?," Energy Policy, Elsevier, vol. 127(C), pages 486-499.
    2. Kristina Mjörnell & Paula Femenías & Kerstin Annadotter, 2019. "Renovation Strategies for Multi-Residential Buildings from the Record Years in Sweden—Profit-Driven or Socioeconomically Responsible?," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
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    4. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    5. Hårsman, Björn & Daghbashyan, Zara & Chaudhary, Parth, 2016. "On the Quality and Impact of Residential Energy Performance Certificates," Working Paper Series in Economics and Institutions of Innovation 429, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    6. Johansson, Tim & Olofsson, Thomas & Mangold, Mikael, 2017. "Development of an energy atlas for renovation of the multifamily building stock in Sweden," Applied Energy, Elsevier, vol. 203(C), pages 723-736.
    7. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
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    2. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.

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