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Jenny Von Platten

Jenny von Platten

Postdoktor

Jenny Von Platten

Using machine learning to enrich building databases : Methods for tailored energy retrofits

Author

  • Jenny Von Platten
  • Claes Sandels
  • Kajsa Jörgensson
  • Viktor Karlsson
  • Mikael Mangold
  • Kristina Mjörnell

Summary, in English

Building databases are important assets when estimating and planning for national energy savings fromenergy 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.

Department/s

  • Division of Building Physics

Publishing year

2020-05-19

Language

English

Publication/Series

Energies

Volume

13

Issue

10

Document type

Journal article

Publisher

MDPI AG

Topic

  • Building Technologies

Keywords

  • Artificial intelligence
  • Building database enrichment
  • Building-specific information
  • Energy performance certificate
  • Energy retrofitting
  • Energy transition
  • Google street view
  • Long-term renovation strategy
  • Machine learning
  • Support vector machine

Status

Published

ISBN/ISSN/Other

  • ISSN: 1996-1073