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

Titelangaben

Wenninger, Simon ; Wiethe, Christian:
Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany.
In: Business & Information Systems Engineering. Bd. 63 (2021) Heft 3 . - S. 223-242.
ISSN 1867-0202
DOI: https://doi.org/10.1007/s12599-021-00691-2

Angaben zu Projekten

Projekttitel:
Offizieller Projekttitel
Projekt-ID
Projektgruppe WI Nachhaltiges Energiemanagement & Mobilität
Ohne Angabe
Projektgruppe WI Künstliche Intelligenz
Ohne Angabe

Abstract

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.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Begutachteter Beitrag: Ja
Keywords: Energy performance certificates; Data-driven methods; Machine learning; Data analytics; Energy quantification methods; Benchmarking; Building energy; Energy informatics
Institutionen der Universität: Fakultäten > Rechts- und Wirtschaftswissenschaftliche Fakultät > Fachgruppe Betriebswirtschaftslehre
Forschungseinrichtungen
Forschungseinrichtungen > Institute in Verbindung mit der Universität
Forschungseinrichtungen > Institute in Verbindung mit der Universität > Projektgruppe Wirtschaftsinformatik der Fraunhofer FIT
Forschungseinrichtungen > Institute in Verbindung mit der Universität > FIM Kernkompetenzzentrum Finanz- & Informationsmanagement
Fakultäten
Fakultäten > Rechts- und Wirtschaftswissenschaftliche Fakultät
Titel an der UBT entstanden: Nein
Themengebiete aus DDC: 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik
300 Sozialwissenschaften > 330 Wirtschaft
Eingestellt am: 30 Mär 2021 08:54
Letzte Änderung: 27 Apr 2022 13:29
URI: https://eref.uni-bayreuth.de/id/eprint/64468