Prediction of short‐term photovoltaic power via codec neural network and mode decomposition based deep learning approach-Reference-Cited by-同舟云学术

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Prediction of short‐term photovoltaic power via codec neural network and mode decomposition based deep learning approach

Published:2021-11-18 Issue: Volume: Page:
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Sci Eng

Author:

Li Jie¹^ORCID,Li RunRan¹,Jia YuanJie¹,Zhang ZhiXin¹

Affiliation:

1. School of Electronic and Control Engineering Chang’an University Xi’an China

Funder

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1009

Reference51 articles.

1. A current perspective on the accuracy of incoming solar energy forecasting

2. Solar photovoltaic generation forecasting methods: a review;Sobri S;Energy Convers Manag,2018

3. Short‐term forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal;Lopes FM;Sol Energy,2018

Cited by 3 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. The Use of Additive Decomposition and Deep Neural Network for Photovoltaic Power Forecasting;2023 31st International Conference on Electrical Engineering (ICEE);2023-05-09

2. Remaining Useful Life Prediction of Turbofan Engines Using CNN-LSTM-SAM Approach;IEEE Sensors Journal;2023-05-01

3. An Optimal Algorithm for Renewable Energy Generation Based on Neural Network;International Journal of Photoenergy;2022-08-27

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