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Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Title data

Wenninger, Simon ; Wiethe, Christian:
Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany.
In: Business & Information Systems Engineering. (2021) .
ISSN 1867-0202
DOI: https://doi.org/10.1007/s12599-021-00691-2

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Energie und kritische InfrastrukturenNo information
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

To achieve ambitious climate goals, it is necessary to increase the rate of purposeful retrofit measures in the building sector. As a result, Energy Performance Certificates have been designed as important evaluation and rating criterion to increase the retrofit rate in the EU and Germany. Yet, today’s most frequently used and legally required methods to quantify building energy performance show low prediction accuracy, as recent research reveals. To enhance prediction accuracy, the research community introduced data-driven methods which obtained promising results. However, there are no insights in how far Energy Quantification Methods are particularly suited for energy performance prediction. In this research article the data-driven methods Artificial Neural Network, D-vine copula quantile regression, Extreme Gradient Boosting, Random Forest, and Support Vector Regression are compared with and validated by real-world Energy Performance Certificates of German residential buildings issued by qualified auditors using the engineering method required by law. The results, tested for robustness and systematic bias, show that all data-driven methods exceed the engineering method by almost 50% in terms of prediction accuracy. In contrast to existing literature favoring Artificial Neural Networks and Support Vector Regression, all tested methods show similar prediction accuracy with marginal advantages for Extreme Gradient Boosting and Support Vector Regression in terms of prediction accuracy. Given the higher prediction accuracy of data-driven methods, it seems appropriate to revise the current legislation prescribing engineering methods. In addition, data-driven methods could support different organizations, e.g., asset management, in decision-making in order to reduce financial risk and to cut expenses.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Energy performance certificates; Data-driven methods; Machine learning; Data analytics; Energy quantification methods; Benchmarking; Building energy; Energy informatics
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 > 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: 30 Mar 2021 08:54
Last Modified: 30 Mar 2021 08:54
URI: https://eref.uni-bayreuth.de/id/eprint/64468