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Leveraging Advanced Ensemble Models to Increase Building Energy Performance Prediction Accuracy in the Residential Building Sector

Title data

Konhäuser, Koray ; Wenninger, Simon ; Werner, Tim ; Wiethe, Christian:
Leveraging Advanced Ensemble Models to Increase Building Energy Performance Prediction Accuracy in the Residential Building Sector.
In: Energy and Buildings. (2022) . - No. 112242.
ISSN 0378-7788
DOI: https://doi.org/10.1016/j.enbuild.2022.112242

Project information

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

Abstract in another language

Accurate predictions for buildings’ energy performance (BEP) are crucial for retrofitting investment decisions and building benchmarking. With the increasing data availability and popularity of ma-chine learning across disciplines, research started to investigate machine learning for BEP predic-tions. While stand-alone machine learning models showed first promising results, a comprehensive analysis of advanced ensemble models to increase prediction accuracy is missing for annual BEP predictions. We implement and thoroughly tune twelve machine learning models to bridge this re-search gap, ranging from stand-alone to homogeneous and heterogeneous ensemble learning mod-els. Based on an extensive real-world dataset of over 25,000 German residential buildings, we benchmark their prediction accuracy. The results provide strong evidence that ensemble models substantially outperform stand-alone machine learning models both on average and in case of the best-performing model. All models are tested for robustness and systematic bias by evaluating their prediction performance along different building age classes, living space bins, and several error measures. Extreme gradient boosting as ensemble model exhibits the highest prediction accuracy, followed by a multilayer perceptron ahead of further ensemble models. We conclude that ensemble models for annual BEP prediction are advantageous compared to stand-alone models and outper-form their results in most cases.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Building Energy performance; Energy quantification methods; Machine learning; Ensemble learning; Artificial Intelligence; Building energy consumption; Supervised learning; Energy efficiency
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: 15 Jun 2022 07:52
Last Modified: 19 Aug 2022 10:28
URI: https://eref.uni-bayreuth.de/id/eprint/70085