Deep Learning based Prediction of Solar Surface Irradiance with Geostationary Satellite Images-Reference-Cited by-同舟云学术

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Deep Learning based Prediction of Solar Surface Irradiance with Geostationary Satellite Images

Published:2022-06-07 Issue: Volume: Page:
ISSN:
Container-title:2022 17th Annual System of Systems Engineering Conference (SOSE)
language:
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Author:

Ahn Hyojung¹,Yu Jeongmin²,Yeom Jong-Min³

Affiliation:

1. Korea Aerospace Research Institute, University of Science and Technology,Daejeon,Republic of Korea

2. Chungnam national university,Dept of computer engineering,Daejeon,Republic of Korea

3. Korea Aerospace Research Institute,Satellite Application Division,Daejeon,Republic of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9812583/9812630/09812657.pdf?arnumber=9812657

Reference22 articles.

1. Machine Learning for Sustainable Energy Systems

2. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

3. Short-term solar irradiance forecasting via satellite/model coupling

4. Prediction of Solar Power Using Near-Real Time Satellite Data

5. Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest

Cited by 1 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. A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used;Applied Sciences;2024-06-27

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