Evaluating strategic retrofit measures for energy-efficient residential buildings with artificial intelligence

• Introducing a novel feature value substitution methodology to simulate potential energy consumption savings for specific retrofit options. • Quantifying energy-savings on large-scale real-world data and solving data scarcity issues by introducing a feature value substitution methodology. • Providing a retrofit index to the research community that combines energy savings, carbon emission reduction, retrofit costs, and local subsidies into a comprehensible measurand. • Mitigating the uncertainty of homeowners and energy auditors in the decision-making process to identify the most suitable retrofit option for individual residential buildings. • Scenario analysis for different retrofit budgets and energy prices to identify the retrofit measure with the highest energy saving potential. 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.

Authors

Institutions

Publication Details

Journal
Energy and Buildings
Published
2026-02-26
DOI
https://doi.org/10.1016/j.enbuild.2026.117205
Primary Topic
Building Energy and Comfort Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating strategic retrofit measures for energy-efficient residential buildings with artificial intelligence

Koray Konhäuser, Nina Schwarz, Tim Werner
Energy and Buildings
Building Energy and Comfort Optimization
article

Evaluating strategic retrofit measures for energy-efficient residential buildings with artificial intelligence

Koray Konhäuser, Nina Schwarz, Tim Werner
article en

Abstract

• Introducing a novel feature value substitution methodology to simulate potential energy consumption savings for specific retrofit options. • Quantifying energy-savings on large-scale real-world data and solving data scarcity issues by introducing a feature value substitution methodology. • Providing a retrofit index to the research community that combines energy savings, carbon emission reduction, retrofit costs, and local subsidies into a comprehensible measurand. • Mitigating the uncertainty of homeowners and energy auditors in the decision-making process to identify the most suitable retrofit option for individual residential buildings. • Scenario analysis for different retrofit budgets and energy prices to identify the retrofit measure with the highest energy saving potential. 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.

Energy and BuildingsVol. 359
Technische Hochschule Augsburg (DE), Fraunhofer Institute for Applied Information Technology (DE)
Affordable and clean energy
Openalex Percentile: Top 43%
Building Energy and Comfort Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.