Short-term photovoltaic power forecasting using parameter-optimized variational mode decomposition and attention-based neural network-Reference-Cited by-同舟云学术

Short-term photovoltaic power forecasting using parameter-optimized variational mode decomposition and attention-based neural network

Published:2024-03-13 Issue:1 Volume:46 Page:3807-3824
ISSN:1556-7036
Container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
language:en
Short-container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects

Author:

Tao Kejun¹²³⁴,Zhao Jinghao²³⁴,Wang Nana²³⁴,Tao Ye¹,Tian Yajun²³⁴

Affiliation:

1. School of Information Science & Technology, Qingdao University of Science and Technology, Qingdao, China

2. Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China

3. Shandong Energy Institute, Qingdao, China

4. Qingdao New Energy Shandong Laboratory, Qingdao, China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15567036.2024.2323158

Reference37 articles.

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3. Financial time series forecasting model based on CEEMDAN and LSTM

4. Interpretable spatio-temporal attention LSTM model for flood forecasting

5. DKASC Alice Springs. 2022. 36 Accessed November 1 2022. https://dkasolarcentre.com.au/source/alice-springs/dka-m17-c-phase

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