Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators-Reference-Cited by-同舟云学术

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Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators

Published:2022-10 Issue: Volume:166 Page:107923
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Cramer Eike^ORCID,Paeleke Leonard^ORCID,Mitsos Alexander,Dahmen Manuel^ORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference64 articles.

1. Towards principled methods for training generative adversarial networks;Arjovsky,2017

2. Wasserstein generative adversarial networks;Arjovsky,2017

3. Ancillary services in great britain during the COVID-19 lockdown: A glimpse of the carbon-free future;Badesa;Appl. Energy,2021

4. Understanding and mitigating exploding inverses in invertible neural networks;Behrmann,2021

5. Introduction to Stochastic Programming;Birge,2011

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1. Multivariate scenario generation of day-ahead electricity prices using normalizing flows;Applied Energy;2024-08

2. A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features;Applied Energy;2024-06

3. Day-Ahead Lot-Sizing Under Uncertainty: An Application to Green Hydrogen Production;Lecture Notes in Computer Science;2024

4. Physics-Informed Machine Learning for Power Grid Frequency Modeling;PRX Energy;2023-10-04

5. Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows;Applied Energy;2023-09

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