Evaluating Strategic Retrofit Measures for Energy-Efficient Residential Buildings With Artificial Intelligence

Title data

Werner, Tim ; Konhäuser, Koray ; Schwarz, Nina:
Evaluating Strategic Retrofit Measures for Energy-Efficient Residential Buildings With Artificial Intelligence.
In: Energy and Buildings. Vol. 359 (2026) . - 117205.
ISSN 0378-7788
DOI: https://doi.org/10.1016/j.enbuild.2026.117205

Official URL:

Abstract in another language

The global building sector is one of the main contributors to annual global greenhouse gas emissions, yet homeowners remain hesitant regarding specific retrofit measures to reduce carbon emissions. This is unsurprising as the link between retrofits that reduce energy consumption and corresponding economic and ecological benefits remains elusive. Therefore, this study addresses the intersection of building energy performance, carbon emission reduction, and financial subsidies by quantifying expected energy savings based on specific energy-related retrofits with a real-world dataset containing 25,000 German residential buildings. The simulated energy savings for specific retrofit measures are based on a novel feature value substitution methodology and three sophisticated machine learning models, namely XGBoost, CatBoost, and LightGBM. This study then combines potential ecological gains, household investment budgets, and expected local governmental subsidies into a single informative yet comprehensible retrofit index to overcome the uncertainty regarding retrofits. The results show that glazing is the most impactful feature for potential energy savings of residential buildings, followed by heating system changes from oil to electric heating pumps. In contrast to the neglectable impact of better facade conditions on building energy performance, roof and wall insulation improvements lead to significantly lower energy consumption. This study underscores potential ecological savings of targeted retrofit measures and enables practitioners to cut expenses and reduce the associated financial risks.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Building Energy Efficiency; Building Energy Savings; Artificial Intelligence; Energy Retrofits; Energy Performance Simulation; Retrofit Subsidies
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management Faculties Faculties > Faculty of Law, Business and Economics
Result of work at the UBT:	No
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	06 Mar 2026 06:34
Last Modified:	30 Apr 2026 13:09
URI:	https://eref.uni-bayreuth.de/id/eprint/96533