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The Influence of Building Energy Performance Prediction Accuracy on Retrofit Rates

Title data

Wiethe, Christian ; Wenninger, Simon:
The Influence of Building Energy Performance Prediction Accuracy on Retrofit Rates.
In: Energy Policy. Vol. 177 (June 2023) . - 113542.
ISSN 0301-4215
DOI: https://doi.org/10.1016/j.enpol.2023.113542

Official URL: Volltext

Project information

Project title:
Project's official title
Project's id
Projektgruppe WI Digital Value Network
No information
Projektgruppe WI Nachhaltiges Energiemanagement & Mobilität
No information

Abstract in another language

The residential building sector bears enormous CO2 emission reduction potential. However, uncertainty connected to financial savings of retrofits, stemming largely from inaccurate building energy performance predictions, forms a significant investment barrier. So far, literature does not sufficiently cover the relationship between prediction accuracy and retrofit rates. Thus, this paper aims to provide guidance for policymakers by setting up an agent-based building stock model to derive this relationship for the German residential building sector. Results indicate that higher prediction accuracies positively affect the retrofit rate. Using data-driven prediction methods established in research significantly increases the retrofit rate by over 70%, from around 0.98% to 1.68% compared to the legally prescribed engineering method. This equals CO2 emission reductions of almost 45 Mt by 2050 for Germany, leading to a surplus in consumer rent of 310.19 mn €, while investments in retrofits increase by about 1.195 bn €. The government benefits from tax payments and saves opportunity costs. The findings allow to estimate the effect of revising current legislation on building energy performance predictions and thereby clearly guide policymakers.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Building stock model; Energy quantification methods; Energy efficiency investments; Machine learning algorithms; Data analytics; Risk
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
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
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: 22 May 2023 06:23
Last Modified: 22 May 2023 06:23
URI: https://eref.uni-bayreuth.de/id/eprint/76482