A 3D indicator for guiding AI applications in the energy sector
Hugo Quest,
Marine Cauz,
Fabian Heymann,
Christian Rod,
Lionel Perret,
Christophe Ballif,
Alessandro Virtuani,
Nicolas Wyrsch
Affiliations
Hugo Quest
École Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical and Micro Engineering (IEM), Photovoltaics and Thin-Film Electronics Laboratory (PV-LAB), Neuchâtel, Switzerland; 3S Swiss Solar Solutions AG, Thun, Switzerland; Corresponding author at: École Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical and Micro Engineering (IEM), Photovoltaics and Thin-Film Electronics Laboratory (PV-LAB), Neuchâtel, Switzerland.
Marine Cauz
École Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical and Micro Engineering (IEM), Photovoltaics and Thin-Film Electronics Laboratory (PV-LAB), Neuchâtel, Switzerland; Planair SA, Yverdon-les-Bains, Switzerland
Fabian Heymann
Swiss Federal Office for Energy (SFOE), Digital Innovation Office, Ittigen, Switzerland
Christian Rod
Planair SA, Yverdon-les-Bains, Switzerland
Lionel Perret
Planair SA, Yverdon-les-Bains, Switzerland
Christophe Ballif
École Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical and Micro Engineering (IEM), Photovoltaics and Thin-Film Electronics Laboratory (PV-LAB), Neuchâtel, Switzerland; Swiss Center for Electronics and Microtechnology (CSEM), Sustainable Energy Center, Neuchâtel, Switzerland
Alessandro Virtuani
École Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical and Micro Engineering (IEM), Photovoltaics and Thin-Film Electronics Laboratory (PV-LAB), Neuchâtel, Switzerland
Nicolas Wyrsch
École Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical and Micro Engineering (IEM), Photovoltaics and Thin-Film Electronics Laboratory (PV-LAB), Neuchâtel, Switzerland
The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, with increasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promising results in research. The growing interest comes from decision makers of both the industry and policy domains, searching for applications to increase companies’ profitability, raise efficiency and facilitate the energy transition. This paper aims to provide a novel three-dimensional (3D) indicator for AI applications in the energy sector, based on their respective maturity level, regulatory risks and potential benefits. Case studies are used to exemplify the application of the 3D indicator, showcasing how the developed framework can be used to filter promising AI applications eligible for governmental funding or business development. In addition, the 3D indicator is used to rank AI applications considering different stakeholder preferences (risk-avoidance, profit-seeking, balanced). These results allow AI applications to be better categorised in the face of rapidly emerging national and intergovernmental AI strategies and regulations that constrain the use of AI applications in critical infrastructures.
