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Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits
RISE Research Institutes of Sweden, Built Environment, System setup and Service Innovation. Lund University, Sweden.
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology.ORCID iD: 0000-0002-9860-4472
Lund University, Sweden.
Lund University, Sweden.
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2020 (English)In: energies, E-ISSN 1996-1073, Vol. 13, no 10Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 13, no 10
Keywords [en]
building database enrichment, machine learning, artificial intelligence, Google Street View, energy performance certificate, support vector machine, energy retrofitting, energy transition, building-specific information, long-term renovation strategy
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-44988DOI: 10.3390/en13102574OAI: oai:DiVA.org:ri-44988DiVA, id: diva2:1431292
Note

(This article belongs to the Special Issue Energy Performance of Buildings)

Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-05-19Bibliographically approved

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Sandels, ClaesMangold, MikaelMjörnell, Kristina
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CiteExportLink to record
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Citation style
  • apa
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Output format
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