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GENERATIVE: 作品数：537被引量：766H指数：13; 导出分析报告; 相关作者：郝志桃王东李昌华李曙光赵凯华更多>>; 相关机构：华中师范大学浙江省交通规划设计研究院有限公司西安建筑科技大学上海电力大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金中国博士后科学基金国家社会科学基金高等学校学科创新引智计划更多>>

Probabilistic simulation of electricity price scenarios using Conditional Generative Adversarial Networks: 《Energy and AI》2024年第4期110-123,共14页Viktor Walter Andreas Wagner; supported by the German Federal Ministry of Education and Research[grant 13FH587KX1](FederatedF orecasts).; A novel approach for generative time series simulation of electricity price scenarios is presented.A"Time Series Simulation Conditional Generative Adversarial Network"(TSS-CGAN)generates short-term electricity price s...; 关键词：Time series simulation Probabilistic modeling Day-ahead electricity prices 1D convolutions Bidirectional long short-term memory Generative adversarial networks

VA-Creator-A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns: 《Energy and AI》2024年第4期160-200,共41页Michael Meiser Benjamin Duppe Ingo Zinnikus Alexander Anisimov; funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK)as part of the ForeSightNEXT project;by the German Federal Ministry of Education and Research(BMBF)as part of the ENGAGE project.; With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning(ML),obtaining power consumption data is becoming more and more important.Collecting real-world energy data using...; 关键词：Smart Home Synthetic Sensor Data Energy data Virtual Appliance Machine Learning Neural Networks Multilayer Perceptron Generative Adversarial Network Dynamic Time Warping Transfer Learning Non-Intrusive Load Monitoring NILMTK

Distributionally robust optimization configuration method for island microgrid considering extreme scenarios被引量：1: 《Energy and AI》2024年第3期179-194,共16页Qingzhu Zhang Yunfei Mu Hongjie Jia Xiaodan Yu Kai Hou; funded by the National Natural Science Foundation of China(Grant/Award Numbers:52177107 and 52222704);Science and Technology Project of Tianjin Municipality,China(22JCZDJC00780).; The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significan...; 关键词：Island microgrid Extreme scenario Distributionally robust optimization Conditi onal generative adversarial network

CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics: 《Energy and AI》2024年第2期14-23,共10页Fengshuo Hu Chaoyu Dong Luyu Tian Yunfei Mu Xiaodan Yu Hongjie Jia; supported by the project of National Natural Science Foundation of China(U23B6006,52277116).; Lithium batteries find extensive applications in energy storage.Temperature is a crucial indicator for assessing the state of lithium-ion batteries,and numerous experiments require thermal images of lithium-ion batter...; 关键词：Lithium-ion batteries Generative adversarial network CWGAN-GP

A generative adversarial network (GAN) approach to creating synthetic flame images from experimental data: 《Energy and AI》2023年第3期14-24,共11页Anthony Carreon Shivam Barwey Venkat Raman; Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows;however,they tend to generate massive data-sets,rendering conventional analysis intractable and inefficient.To a...; 关键词：Generative adversarial network Combustion modeling Data-driven modeling

SolarGAN:Synthetic annual solar irradiance time series on urban building facades via Deep Generative被引量：1: 《Energy and AI》2023年第2期1-21,共21页Yufei Zhang Arno Schlueter Christoph Waibel; Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems viaharnessing solar energy available on building envelopes. While methods to assess solar irradiation, especiallyo...; 关键词：Urban solar potential Data-driven Deep Generative Networks(DGN) Building-integrated photovoltaic(BIPV) Generative Adversarial Network(GAN) Variational Autoencoder(VAE)

Deep Neural Network-Based Generation of Planar CH Distribution through Flame Chemiluminescence in Premixed Turbulent Flame: 《Energy and AI》2023年第2期22-30,共9页Lei Han Qiang Gao Dayuan Zhang Zhanyu Feng Zhiwei Sun Bo Li Zhongshan Li; supported by the National Natural Science Foundation of China(Grant No.52176169,52276164)。; Flame front structure is one of the most fundamental characteristics and, hence, vital for understanding combustion processes. Measuring flame front structure in turbulent flames usually needs laser-based diagnostic t...; 关键词：Turbulent flame front Neural network Conditional generative adversarial nets Laser diagnostics CHEMILUMINESCENCE

Conditional Generative Adversarial Networks for modelling fuel sprays: 《Energy and AI》2023年第2期62-75,共14页Cihan Ates Farhad Karwan Max Okraschevski Rainer Koch Hans-Jörg Bauer; In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs)was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. Themodel co...; 关键词：Generative Adversarial Networks Generative learning Fuel injection Aero engines Multivariate time series

Temporally resolving premixed turbulent flame structures using self-supervised adversarial reconstruction of CH-PLIF: 《Energy and AI》2023年第1期51-62,共12页Ji-Hun Oh Aaron W.Skiba Stephen D.Hammack Constandinos M.Mitsingas Campbell D.Carter Tonghun Lee; supported by the Army Research Laboratory under Cooperative Agreement Number.W911NF-20-2-0220;Student support and data was also provided by AFOSR(FA9550-21-1-0072,Program Manager:Dr.Chiping Li);ONR(N00014-21-1-2475,Program Manager:Dr.Eric Marineau).; Understanding the turbulence-flame interaction is crucial to model the low-emission combustors developed for energy and propulsion applications. To this end, a novel frame interpolation (FI) method is proposed to bett...; 关键词：Self-supervised frame interpolation Premixed turbulent flames Planar laser-induced fluorescence Generative adversarial networks Pocket behavior

Stress testing electrical grids:Generative Adversarial Networks for load scenarion: 《Energy and AI》2022年第3期182-192,共11页Matteo Rizzato Nicolas Morizet William Maréchal Christophe Geissler; As the energy transition is upon us,the replacement of combustion engines by electrical ones will imply a greater stress on the electrical grid of different countries.Therefore,it is of paramount importance to simulat...; 关键词：Generative Adversarial Networks Artificial data Electrical grid SIMULATION Load profiles

GENERATIVE