Artificial intelligence driving perception,cognition,decision‐making and deduction in energy systems:State‐of‐the‐art and potential directions  

作　　者：ZhaoYang Dong Tianjing Wang 

机构地区：[1]Department of Electrical Engineering,City University of Hong Kong,Kowloon,Hong Kong,China [2]School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,Singapore

出　　处：《Energy Internet》2024年第1期27-33,共7页能源互联网（英文）

基　　金：MOE Tier 1 Projects,Grant/Award Numbers:RT9/22,RG59/2;City University of Hong Kong,Grant/Award Number:Start‐Up Grant and STEM Professorship。

摘　　要：In the context of energy systems,managing the complex interplay between diverse power sources and dynamic demands is crucial.With a focus on smart grid technology,continuously innovating artificial intelligence(AI)algorithms,such as deep learning,reinforcement learning,and large language model technologies,have been or have the potential to be leveraged to predict energy consumption patterns,enhance grid operation,and manage distributed energy resources efficiently.These capabilities are essential to meet the requirements of perception,cognition,decision‐making,and deduction in en-ergy systems.Nevertheless,there are some critical challenges in efficiency,interpretability,transferability,stability,economy,and robustness.To overcome these challenges,we propose critical potential directions in future research,including reasonable sample generation,training models with small datasets,enhancing transfer ability,combining with physics models,collective generative pre‐trained transformer‐agents,multiple foundation models,and improving system robustness,to make advancing AI technologies more suitable for practical engineering.

关 键 词：artificial intelligence COGNITION decision‐making DEDUCTION energy systems PERCEPTION 

分 类 号：TM73[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]