A 3D indicator for guiding AI applications in the energy sector

作     者：Hugo Quest Marine Cauz Fabian Heymann Christian Rod Lionel Perret Christophe Ballif Alessandro Virtuani Nicolas Wyrsch 

作者机构：École Polytechnique Fédérale de Lausanne(EPFL)Institute of Electrical and Micro Engineering(IEM)Photovoltaics and Thin-Film Electronics Laboratory(PV-LAB)NeuchâtelSwitzerland S Swiss Solar Solutions AGThunSwitzerland Planair SAYverdon-les-BainsSwitzerland Swiss Federal Office for Energy(SFOE)Digital Innovation OfficeIttigenSwitzerland Swiss Center for Electronics and Microtechnology(CSEM)Sustainable Energy CenterNeuchâtelSwitzerland 

出 版 物：《能源与人工智能(英文)》 (Energy and AI)

年 卷 期：2022年第9卷第3期

页      面：65-77页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：3S Swiss Solar Solutions AG Planair SA SCCER-FURIES Swiss Centre for Competence in Energy Research on the Future Swiss Electrical Infrastructure Innosuisse - Schweizerische Agentur für Innovationsförderung, Innosuisse 

主　　题：Artificial intelligence Digitalisation AI application Big data AI policy Energy sector 

摘      要：The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, withincreasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promisingresults in research. The growing interest comes from decision makers of both the industry and policydomains, searching for applications to increase companies’ profitability, raise efficiency and facilitate theenergy transition. This paper aims to provide a novel three-dimensional (3D) indicator for AI applicationsin the energy sector, based on their respective maturity level, regulatory risks and potential benefits. Casestudies are used to exemplify the application of the 3D indicator, showcasing how the developed frameworkcan be used to filter promising AI applications eligible for governmental funding or business *** 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 theface of rapidly emerging national and intergovernmental AI strategies and regulations that constrain the useof AI applications in critical infrastructures.