《Energy and AI》

作品数：411被引量：296H指数：8
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《Energy and AI》
主办单位：天津大学
最新期次：2024年4期更多>>
发文主题：MACHINE_LEARNINGREINFORCEMENT_LEARNINGDEEPARTIFICIAL_NEURAL_NETWORKUSING更多>>
发文领域：自动化与计算机技术电气工程理学经济管理更多>>
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Multi-objective decoupling control of thermal management system for PEM fuel cell
《Energy and AI》2024年第4期410-430,共21页Jun-Hong Chen Pu He Ze-Hong He Jia-Le Song Xian-Hao Liu Yun-Tian Xiao Ming-Yang Wang Lu-Zheng Yang Yu-Tong Mu Wen-Quan Tao 
supported by the National key research and development project of China(Grant No.2023YFB4005803);the Youth Project of the National Natural Science Foundation of China(Grant No.52306113);the Project of Shaanxi Innovative Talent Promotion Plan-Technology Innovation Team(No.2024RS-CXTD-35).
Operating temperature is an important factor that affects the efficiency,durability,and safety of proton exchange membrane fuel cells(PEMFC).Thus,a thermal management system is necessary for controlling the appropriat...
关键词：Proton exchange membrane fuel cell Novel thermal management system Temperature control Decoupling control Multi-objective optimization 
A self-growth convolution network for thermal and mechanical fault detection with very limited engine data
《Energy and AI》2024年第4期431-444,共14页Gou Xin Zhu Xiaolong Wang Xinwei Wang Hui Zhang Junhong Lin Jiewei 
support of the National Key R&D Program of China(Grant No.2021YFD2000303);the Weichai Power Co.,Ltd(WCDL-GH-2023-0147).
Severe faults occur infrequently but are critical for the prognostics and health management(PHM)of power machinery.Due to the scarcity of fault data,diagnostic models are always facing a very limited data problem.Basi...
关键词：Diesel engine Small-sample fault diagnosis Deep learning Self-growth convolution networks Prognostic health management 
Photovoltaic power forecasting:A Transformer based framework
《Energy and AI》2024年第4期445-458,共14页Gabriele Piantadosi Sofia Dutto Antonio Galli Saverio De Vito Carlo Sansone Girolamo Di Francia 
funding from Ministero per lo Sviluppo nomico,Eco-Fondo per la Crescita Sostenibile,under the framework cordi"c-per l’innovazione di cui al D.M.31 Dicembre 2021 e DD 18 Marzo 2022",project MARTA,n.:F/310193/01-02/X56;It was also supported in part by the Piano Nazionale Ripresa silienza Re-(PNRR)Ministero dell’Universitàe della Ricerca(MUR)Project under Grant PE0000013-FAIR.
The accurate prediction of photovoltaic(PV)energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market....
关键词：PHOTOVOLTAIC Forecasting Deep learning TRANSFORMERS 
Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning
《Energy and AI》2024年第4期459-469,共11页Daniel C.May Matthew Taylor Petr Musilek 
supported by the Natural Sciences and Engineering Research Council(NSERC)of Canada grant RGPIN-2024-04565;by the NSERC/Alberta Innovates grant ALLRP 561116-20;Part of this work has taken place in the Intelligent Robot Learning(IRL)Lab at the University of Alberta,which is supported in part by research grants from the Alberta Machine Intelligence Institute(Amii),Canada;a Canada CIFAR AI Chair,Amii;Digital Research Alliance of Canada;Huawei;Mitacs,Canada;and NSERC,Canada.
As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid.One significant aspect of...
关键词：Reinforcement learning Deep reinforcement learning Distributed energy resources Local energy markets Demand response Distributed energy resource management Transactive energy 
Real-world validation of safe reinforcement learning,model predictive control and decision tree-based home energy management systems
《Energy and AI》2024年第4期470-488,共19页Julian Ruddick Glenn Ceusters Gilles Van Kriekinge Evgenii Genov Cedric De Cauwer Thierry Coosemans Maarten Messagie 
supported by the ECOFLEX project funded by FOD Economie,K.M.O.,Middenstand en Energie,by the ICON project OPTIMESH(FLUX50 ICON Project Collaboration Agreement-HBC.2021.0395)funded by VLAIO;by the Baekeland project SLIMness(HBC.2019.2613)funded by ABB n.v.and VLAIO in equal parts.
Recent advancements in machine learning based energy management approaches,specifically reinforcement learning with a safety layer(OptLayerPolicy)and a metaheuristic algorithm generating a decision tree control policy...
关键词：Energy management system Machine learning Reinforcement learning Decision tree Model predictive control HARDWARE-IN-THE-LOOP Implementation Experimental 
Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU
《Energy and AI》2024年第4期489-505,共17页Pascal Riedel Kaouther Belkilani Manfred Reichert Gerd Heilscher Reinhold von Schwerin 
supported by the project InterBDL(Project funding indicator 01MV23025A)and Project OrPHEus(Project No.608930);The data preparation process was further supported by David Gögelein,a research associate of the Technical University of Applied Sciences at Ulm.
Given the inherent fluctuation of photovoltaic(PV)generation,accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations.Data-driven methods facilitate efficient supply and dem...
关键词：Federated learning Deep learning Recurrent neural networks Data privacy Solar power forecasting Smart grid Residential photovoltaic 
Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia
《Energy and AI》2024年第4期506-517,共12页Zhihao Xing Rodolfo S.M.Freitas Xi Jiang 
supported by UK Physical Sciences Research Council(EPSRC)under Grant No.EP/X019551/1;Supercomputing time on ARCHER is provided by the“UK Consortium on Mesoscale Engineering Sciences(UKCOMES)”under the UK EPSRC Grant No.EP/R029598/1.
This study developed neural network potentials(NNPs)specifically tailored for pure ethylene and ethyleneammonia blended systems for the first time.The NNPs were trained on a dataset generated from density func-tional ...
关键词：AMMONIA ETHYLENE SOOT Neural network potential Reactive molecular dynamics 
Machine learning for battery quality classification and lifetime prediction using formation data
《Energy and AI》2024年第4期518-530,共13页Jiayu Zou Yingbo Gao Moritz H.Frieges Martin F.Börner Achim Kampker Weihan Li 
funding from the research project“SPEED”(03XP0585)funded by the German Federal Ministry of Education and Research(BMBF);Part of the work was done within the research project"Model2life"(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF).
Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classif...
关键词：BATTERY FORMATION Quality classification Life prediction Machine learning 
Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems
《Energy and AI》2024年第4期531-549,共19页Ammar M.Bahman Emil Pradeep Zafar Said Prabhakar Sharma 
Lubricants for compressor oil significantly enhance the energy efficiency and performance of heat pump(HP)systems.This study compares prognostic machine learning(ML)models designed to predict the thermal conduc-tivity...
关键词：HP compressor Nanolubricant NANODIAMOND Prognostic ML GPR BRT 
Optimization of a Bayesian game for Peer-to-Peer trading among prosumers under incomplete information via a CNN-LSTM-ATT
《Energy and AI》2024年第4期550-561,共12页Hongjie Jia Wanxin Tang Xiaolong Jin Yunfei Mu Dengxin Ai Xiaodan Yu Wei Wei 
supported by the project of Science and Technology Project of the State Grid Corporation of China(1400-202312333A-1-1-ZN).
In modern low-carbon industrial parks,various distributed renewable energy resources are employed to fulfill production needs.Despite the growing capacity of renewable energy generation,a significant portion of the po...
关键词：Bayesian game Electricity energy price forecasting Peer-to-peer transaction Renewable energy consumption Thermal dynamics 
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