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Artificial Intelligence in Energy Demand Response : A Taxonomy of Input Data Requirements

Title data

Michaelis, Anne ; Halbrügge, Stephanie ; Körner, Marc-Fabian ; Fridgen, Gilbert ; Weibelzahl, Martin:
Artificial Intelligence in Energy Demand Response : A Taxonomy of Input Data Requirements.
In: Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI). - Nürnberg, Germany , 2022

Official URL: Volltext

Project information

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

Abstract in another language

The ongoing energy transition increases the share of renewable energy sources (RES). To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Energy Informatics; Green IS; Demand Response; Artificial Intelligence; Input Data Requirements
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: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 01 Dec 2021 09:45
Last Modified: 04 Aug 2022 07:02
URI: https://eref.uni-bayreuth.de/id/eprint/68049