Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Evidence for residential building retrofitting practices using explainable AI and socio-demographic data

Title data

Wenninger, Simon ; Karnebogen, Philip ; Lehmann, Sven ; Menzinger, Tristan ; Reckstadt, Michelle:
Evidence for residential building retrofitting practices using explainable AI and socio-demographic data.
In: Energy Reports. (2022) .
ISSN 2352-4847
DOI: https://doi.org/10.1016/j.egyr.2022.10.060

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Digital Value NetworkNo information
Projektgruppe WI Nachhaltiges Energiemanagement & MobilitätNo information

Abstract in another language

Extensive retrofits and effective policy measures are needed to meet the ambitious climate goals, particularly in the UK, with the EU's oldest residential building stock. Researchers must investigate the factors influencing retrofits to enable effective and targeted policy measures. To date, however, there is a lack of holistically large-scale quantitative studies accounting for such factors. At the same time, great potential is seen in data-driven solutions and the use of explainable artificial intelligence (XAI). We address this research gap by combining supervised machine learning with XAI employing a three-stage approach: First, we consolidate datasets of Energy Performance Certificates from England and Wales from which we extract conducted retrofits, house prices, and socio-demographic information. Second, we apply an eXtreme Gradient Boosting (XGBoost) model that predicts whether a building has been retrofitted or not. Lastly, we use SHapley Additive exPlana-tions values (SHAP) as an XAI technique to identify the key factors and relationships that influence the implementation of retrofits. We succeed in substantiating results previously obtained in quali-tative or small-scale studies and also find that retrofit-related policies already implemented in re-gional cases, such as the "Better Homes for Yorkshire” initiative, can successfully achieve large-scale success through replication in other regions. Further, our results suggest the implementation of income-based CO2 taxes as a reasonable and easy-to-implement policy measure.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Energy Performance Certificates; Retrofitting; Energy Efficiency Policy; Explainable AI; Data Analytics; Policy Implications
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Professor Information Systems and Digital Energy Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Professor Information Systems and Digital Energy Management > Professor Information Systems and Digital Energy Management - Univ.-Prof. Dr. Jens Strüker
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management > Chair Information Systems and Value-Based Business Process Management - Univ.-Prof. Dr. Maximilian Röglinger
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 20 Oct 2022 08:21
Last Modified: 31 Oct 2022 06:15
URI: https://eref.uni-bayreuth.de/id/eprint/72475