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CORRECTION article

Front. Energy Res.

Sec. Solar Energy

Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1707498

Correction: Short-time Photovoltaic output prediction method based on Depthwise Separable Convolution Visual Geometry Group-Deep Gate Recurrent Neural Network

Provisionally accepted
Lei ZhangLei Zhang1Shuang ZhaoShuang Zhao2Guanchao ZhaoGuanchao Zhao1Lingyi WangLingyi Wang2Baolin LiuBaolin Liu1Zhimin NaZhimin Na2Zhijian LiuZhijian Liu3*Zhongming YuZhongming Yu3Wei HeWei He3
  • 1Yunnan Power Grid Co., Ltd., Qujing Power Supply Bureau, Qujing, China
  • 2Yunnan Power Grid Corporation Planning and Construction Research Center, Kunming, China
  • 3Kunming University of Science and Technology, Kunming, China

This article is a correction to:

  1. Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network

    1. Read original article

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Correction on: To overcome these challenges, this paper utilizes the Exponential Linear Units (ELU) activation function, introduced by Clevert et al., in 2016. ELU maintains nonlinearity while providing a better handling of negative inputs, avoiding the "dying ReLU" problem (Staer-Jensen et al., 2018). This characteristic has made ELU popular in deep neural networks, particularly in natural language processing and image processing, where it has achieved notable results. The reference for [Staer-Jensen et al., 2018] was erroneously written as [Staer-Jensen, H., Sunde, K., Nakstad, E. R., Eritsland, J., and Andersen, G. Ø . (2018). Comparison of Three Haemodynamic Monitoring Methods in Comatose Post Cardiac Arrest Patients. Scand. Cardiovasc. J. 52 (3), 141–148. doi:10.1080/14017431.2018.1450992]. It should be [Clevert, D.-A., Untertiner, T., and Hochreiter, S. (2016). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv preprint. arXiv: 1511.07289, 4.5, 11].

Keywords: photovoltaic power outputprediction, deep learning, depthwiseseparable convolution, VGG, Gate Recurrent Neural Network

Received: 17 Sep 2025; Accepted: 10 Oct 2025.

Copyright: © 2025 Zhang, Zhao, Zhao, Wang, Liu, Na, Liu, Yu and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Zhijian Liu, 248400248@qq.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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