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

Jenny von Platten, Claes Sandels, Kajsa Jörgensson, Viktor Karlsson, Mikael Mangold and Kristina Mjörnell
Additional contact information
Jenny von Platten: Division of Built Environment, RISE Research Institutes of Sweden, Sven Hultins plats 5, 412 58 Gothenburg, 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

Energies, 2020, vol. 13, issue 10, 1-22

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.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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