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Explainable long-term building energy consumption prediction using QLattice

Title data

Wenninger, Simon ; Kaymacki, Can ; Wiethe, Christian:
Explainable long-term building energy consumption prediction using QLattice.
In: Applied Energy. Vol. 308 (15 February 2022) . - No. 118300.
ISSN 1872-9118
DOI: https://doi.org/10.1016/j.apenergy.2021.118300

Project information

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

Abstract in another language

The global building sector is responsible for nearly 40% of total carbon emissions, offering great potential to move closer to set climate goals. Energy performance certificates designed to increase the energy efficiency of buildings require accurate predictions of building energy performance. With significant advances in information and communication technology, data-driven methods have been introduced into building energy performance research demonstrating high computational efficiency and prediction performance. However, most studies focus on prediction performance without considering the potential of explainable artificial intelligence. To bridge this gap, the novel QLattice algorithm, designed to satisfy both aspects, is applied to a dataset of over 25,000 German residential buildings for predicting annual building energy performance. The prediction performance, computation time, and explainability of the QLattice is compared to the established machine learning algorithms artificial neural network, support vector regression, extreme gradient boosting, and multiple-linear regression in a case study, variable importance analyzed, and appropriate applications proposed. The results show quite strongly, that the QLattice should be further considered in the research of energy performance certificates and may be a potential alternative to established machine learning algorithms for other prediction tasks in energy research.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Building energy performance; Energy quantification methods; Energy performance certificates; Explainable AI; Machine learning algorithms; QLattice
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: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 10 Jan 2022 13:57
Last Modified: 10 Jan 2022 13:57
URI: https://eref.uni-bayreuth.de/id/eprint/68259