The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods.In thi...The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods.In this context,Graph Neural Networks are a promising solution due to their ability to learn from graphstructured data.Combined with Reinforcement Learning,they can be used as control approaches to determine remedial actions.This review analyzes how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications,particularly transmission and distribution grids.We analyze the reviewed approaches in terms of the graph structure,the Graph Neural Network architecture,and the Reinforcement Learning approach.Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data,its current stage is primarily proof-of-concept,and it is not yet deployable to real-world applications.We highlight the open challenges and limitations for real-world applications.展开更多
It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identificatio...It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.展开更多
Artificial intelligence(AI)for mineral prospectivity mapping(MPM)is a promising frontier in mineral exploration.However,due to the complexity of mineralization processes,the rarity of mineralization events,the diversi...Artificial intelligence(AI)for mineral prospectivity mapping(MPM)is a promising frontier in mineral exploration.However,due to the complexity of mineralization processes,the rarity of mineralization events,the diversity of mineralization features,and the black-box nature of AI,intelligent MPM faces several challenges,including inadequate representation of geological prospecting data and their spatial coupling relationships,insufficient training samples,poor model robustness,limited generalization capability,and lack of interpretability.This study systematically analyzes the underlying causes of these challenges in MPM and reviews previously proposed solutions.Accordingly,two novel AI models for MPM are proposed to address the above-mentioned issues:(1)a geologically constrained self-supervised Graph-Transformer model has the ability to mitigate the influence of limited labeled data by leveraging self-supervised learning.This model utilizes the graph structure to capture the spatial coupling between geological entities,and enhances the ability to model long-range spatial dependencies through the Transformer architecture;and(2)a geologically constrained graph reinforcement learning(RL)model can use the graph structure to represent geological features and comprehensively mine data through RL mechanisms.Additionally,geological knowledge is embedded into the reward mechanism of RL to incorporate mineralization knowledge into state discrimination,thereby enhancing its generalization ability and interpretability.展开更多
基金Graph Neural Networks for Grid Control(GNN4GC),founded by the Federal Ministry for Economic Affairs and Climate Action,Germany,under the funding code 03EI6117 A.AI4REALNET has received funding from European Union’s Horizon Europe Research and Innovation program under the Grant Agreement No 101119527.+1 种基金Reinforcement Learning for Cognitive Energy Systems(RL4CES)from the Intelligent Embedded Systems of the University Kassel and Fraunhofer IEE funded by the Ministry of Education and Research Germany(BMBF)unter the funding code 01IS22063 A.GAIN project funded by the Ministry of Education and Research Germany(BMBF),under the funding code 01IS20047 A,according to the’Policy for the funding of female junior researchers in Artificial Intelligence’.
文摘The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods.In this context,Graph Neural Networks are a promising solution due to their ability to learn from graphstructured data.Combined with Reinforcement Learning,they can be used as control approaches to determine remedial actions.This review analyzes how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications,particularly transmission and distribution grids.We analyze the reviewed approaches in terms of the graph structure,the Graph Neural Network architecture,and the Reinforcement Learning approach.Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data,its current stage is primarily proof-of-concept,and it is not yet deployable to real-world applications.We highlight the open challenges and limitations for real-world applications.
基金supported by State Grid Corporation of China Project“Research on Coordinated Strategy of Multi-type Controllable Resources Based on Collective Intelligence in an Energy”(5100-202055479A-0-0-00).
文摘It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.
基金supported by the National Natural Science Foundation of China(Grant Nos.42425208,42321001)the Natural Science Foundation of Hubei Province(China)(Grant No.2023AFA001)the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(Grant No.MSFGPMR2025-401)。
文摘Artificial intelligence(AI)for mineral prospectivity mapping(MPM)is a promising frontier in mineral exploration.However,due to the complexity of mineralization processes,the rarity of mineralization events,the diversity of mineralization features,and the black-box nature of AI,intelligent MPM faces several challenges,including inadequate representation of geological prospecting data and their spatial coupling relationships,insufficient training samples,poor model robustness,limited generalization capability,and lack of interpretability.This study systematically analyzes the underlying causes of these challenges in MPM and reviews previously proposed solutions.Accordingly,two novel AI models for MPM are proposed to address the above-mentioned issues:(1)a geologically constrained self-supervised Graph-Transformer model has the ability to mitigate the influence of limited labeled data by leveraging self-supervised learning.This model utilizes the graph structure to capture the spatial coupling between geological entities,and enhances the ability to model long-range spatial dependencies through the Transformer architecture;and(2)a geologically constrained graph reinforcement learning(RL)model can use the graph structure to represent geological features and comprehensively mine data through RL mechanisms.Additionally,geological knowledge is embedded into the reward mechanism of RL to incorporate mineralization knowledge into state discrimination,thereby enhancing its generalization ability and interpretability.
