Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning-Reference-Cited by-同舟云学术

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Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning

Published:2024-06 Issue: Volume:309 Page:118398
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Paletta Quentin^ORCID,Nie Yuhao^ORCID,Saint-Drenan Yves-Marie,Le Saux Bertrand^ORCID

Publisher

Elsevier BV

Reference73 articles.

1. What does the energy industry require from meteorology?;Dubus,2018

2. Power systems in transition: Challenges and opportunities ahead for electricity security;International Energy Agency,2020

3. Renewable electricity and the grid: The challenge of variability;Boyle,2009

4. Handling renewable energy variability and uncertainty in power systems operation;Bessa;WIREs Energy Environ,2014

5. Renewable energy integration: Practical management of variability, uncertainty, and flexibility in power grids;Jones,2017

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. Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning;Applied Energy;2024-09

2. SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT;Advances in Applied Energy;2024-07

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