Stochastic Physics-Informed Deep Generative Network Scenario Generation: Application on Responsive Residential Load Management-Reference-Cited by-同舟云学术

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Stochastic Physics-Informed Deep Generative Network Scenario Generation: Application on Responsive Residential Load Management

Published:2023-12-03 Issue: Volume:1 Page:1-6
ISSN:
Container-title:2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)
language:
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Author:

Afrasiabi Mousa¹,Afrasiabi Shahabodin²,Allahmoradi Sarah²,Aghaei Jamshid¹,Chung C.Y³

Affiliation:

1. Lappeenranta University of Technology,School of Energy Systems,Department of Electrical Engineering,Lappeenranta,Finland

2. University of Saskatchewan,Department of Electrical and Computer Engineering,Saskatoon,Canada

3. The Hing Kong Polytechnic University,Department of Electrical Engineering,Hong Kong,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10406302/10407105/10407462.pdf?arnumber=10407462

Reference23 articles.

1. Machine Learning-Based Condition Monitoring of Solar Photovoltaic Systems: A Review

2. Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network

3. Probability density function forecasting of electricity price: Deep gabor convolutional mixture network

4. A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

5. Comprehensive Review of the Recent Advances in Industrial and Commercial DR

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-Agent Safe Reinforcement Learning Based Real-Time Volt/Var Optimization for Modern Distribution Networks;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

2. Normalizing Flows-based Probabilistic Learning-Aided Distribution System State Estimation;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

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