Physics-Informed Machine Learning for Power Grid Frequency Modeling-Reference-Cited by-同舟云学术

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Physics-Informed Machine Learning for Power Grid Frequency Modeling

Published:2023-10-04 Issue:4 Volume:2 Page:
ISSN:2768-5608
Container-title:PRX Energy
language:en
Short-container-title:PRX Energy

Author:

Kruse Johannes^ORCID,Cramer Eike^ORCID,Schäfer Benjamin^ORCID,Witthaut Dirk^ORCID

Funder

German Federal Ministry of Education and Research

Marie Skłodowska-Curie

Forschungszentrum Jülich

Helmholtz Association of German Research Centres

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PRXEnergy.2.043003/fulltext

Reference44 articles.

1. A roadmap for rapid decarbonization

2. Energy system transformations for limiting end-of-century warming to below 1.5 °C

3. The increasing impact of weather on electricity supply and demand

4. Flexibility From Energy Systems Integration: Supporting Synergies Among Sectors

5. Collective nonlinear dynamics and self-organization in decentralized power grids

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. Patterns and correlations in European electricity prices;Chaos: An Interdisciplinary Journal of Nonlinear Science;2024-07-01

2. Feasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers;The 15th ACM International Conference on Future and Sustainable Energy Systems;2024-05-31

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