Long-term Solar Radiation Forecasting using a Deep Learning Approach-GRUs-Reference-Cited by-同舟云学术

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Long-term Solar Radiation Forecasting using a Deep Learning Approach-GRUs

Published:2019-10-21 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP)
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Author:

Aslam Muhammad,Seung Kim Hyung,Jae Lee Seung,Lee Jae-Myeong,Hong Sugwon,Lee Eui Hyang

Funder

Korea Institute of Energy Technology Evaluation and Planning

Korea Electric Power Corporation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9220006/9224631/09224661.pdf?arnumber=9224661

Cited by 10 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. Hybrid deep learning models for time series forecasting of solar power;Neural Computing and Applications;2024-02-22

2. A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation;Renewable Energy;2024-02

3. The Regional Distributed PV Ultra-short-term Power Forecasting Based on Static-dynamic Spatiotemporal Correlation Modeling;2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG);2023-12-03

4. Error revision during morning period for deep learning and multi-variable historical data-based day-ahead solar irradiance forecast: towards a more accurate daytime forecast;Earth Science Informatics;2023-06-06

5. A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives;Energy;2023-01

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