Source-Load Scenario Generation Based on Weakly Supervised Adversarial Learning and Its Data-Driven Application in Energy Storage Capacity Sizing
Author:
Affiliation:
1. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, China
Funder
National Natural Science Foundation of China
National Key Laboratory Open Project of Smart Grid Protection and Operation Control of NARI Group
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Renewable Energy, Sustainability and the Environment
Link
http://xplorestaging.ieee.org/ielx7/5165391/10248410/10076917.pdf?arnumber=10076917
Reference39 articles.
1. Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
2. Privacy-Preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach
3. Data-driven chance constrained stochastic program
4. Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms
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