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

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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

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Abstract

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.