Convolution-Based Sequence to Sequence Model for the Next-Day Photovoltaic Power Forecasting-Reference-Cited by-同舟云学术

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Convolution-Based Sequence to Sequence Model for the Next-Day Photovoltaic Power Forecasting

Published:2024-05-25 Issue: Volume:54 Page:1765-1770
ISSN:
Container-title:2024 36th Chinese Control and Decision Conference (CCDC)
language:
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Author:

Lai Zefeng¹,Hong Liu²,Wang Song¹,Ling Qiang³

Affiliation:

1. University of Science and Technology of China,Department of Automation,Hefei,China

2. Research and Development SNEGRID Technology Co., Ltd.,Hefei,China

3. Hefei Comprehensive National Science Center,Institute of Artificial Intelligence,Hefei,China

Funder

Technology Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587298/10587321/10588092.pdf?arnumber=10588092

Reference17 articles.

1. Neural network ensemble-based solar power generation short-term forecasting;Chaouachi;World Academy of Science, Engineering and Technology,2009

2. Hybrid prediction method of solar power using different computational intelligence algorithms;Hossain

3. Short-term output power forecasting of photovoltaic systems based on the deep belief net

4. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

5. ARIMA and regression models for prediction of daily and monthly clearness index

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