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How Sustainable is Machine Learning in Energy Applications? The Sustainable Machine Learning Balance Sheet

Title data

Wenninger, Simon ; Kaymakci, Can ; Wiethe, Christian ; Römmelt, Jörg ; Baur, Lukas ; Häckel, Björn ; Sauer, Alexander:
How Sustainable is Machine Learning in Energy Applications? The Sustainable Machine Learning Balance Sheet.
In: Wirtschaftsinformatik 2022 Proceedings. - Nürnberg , 2022

Official URL: Volltext

Abstract in another language

Information Systems play a central role in the energy sector to achieve climate targets. With increasing digitization and data availability in the energy sector, data-driven machine learning (ML) approaches emerged, showing high potential. So far, research focused on optimizing ML approaches’ prediction performance. However, this is a one-sided perspective. ML approaches require large computation times and capacities leading to high energy consumption. With the goal of sustainable energy systems, research on ML approaches must be extended to include the component of energy consumption of the actual application. ML solutions must be designed in such a way that the resulting savings in energy (and emissions) are greater than the energy consumption caused using the ML solution. To address this need, we develop the Sustainable Machine Learning Balance Sheet as a framework allowing to holistically evaluate and develop sustainable ML solutions which we validated in a case study and through expert interviews.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Machine Learning; Sustainability; Green IS; Data-driven Approaches; 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
Faculties
Faculties > Faculty of Law, Business and Economics
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: 16 Mar 2022 07:15
Last Modified: 16 Mar 2022 07:15
URI: https://eref.uni-bayreuth.de/id/eprint/68926