Reference : Artificial Intelligence in Energy Demand Response : A Taxonomy of Input Data Requirements
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Energy
Business & economic sciences : Management information systems
Sustainable Development; Security, Reliability and Trust
http://hdl.handle.net/10993/53785
Artificial Intelligence in Energy Demand Response : A Taxonomy of Input Data Requirements
English
Michaelis, Anne [> >]
Halbrügge, Stephanie [> >]
Körner, Marc-Fabian [> >]
Fridgen, Gilbert mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX]
Weibelzahl, Martin [> >]
2022
Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI)
Yes
Nürnberg, Germany
17th International Conference on Wirtschaftsinformatik (WI)
from 21-02-2022 to 23-02-2022
[en] Energy Informatics ; Green IS ; Demand Response ; Artificial Intelligence ; Input Data Requirements
[en] The ongoing energy transition increases the share of renewable energy sources. 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.
http://hdl.handle.net/10993/53785
https://aisel.aisnet.org/wi2022/sustainable_it/sustainable_it/4/
FnR ; FNR13342933 > Gilbert Fridgen > DFS > Paypal-fnr Pearl Chair In Digital Financial Services > 01/01/2020 > 31/12/2024 > 2019

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