Lei Zhang1Shuang Zhao2Guanchao Zhao1Lingyi Wang2Baolin Liu1Zhimin Na2Zhijian Liu3*Zhongming Yu3
Wei He3- 1Yunnan Power Grid Co., Ltd., Qujing Power Supply Bureau, Qujing, China
- 2Yunnan Power Grid Corporation Planning and Construction Research Center, Kunming, China
- 3Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China
by Zhang L, Zhao S, Zhao G, Wang L, Liu B, Na Z, Liu Z, Yu Z and He W (2024). Front. Energy Res. 12:1447116. doi: 10.3389/fenrg.2024.1447116
Clevert, D.-A., Untertiner, T., and Hochreiter, S. (2016). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv [Preprint]. Available online at: https://doi.org/10.48550/arXiv.1511.07289 was not cited in the article. The citation has now been inserted in section 3 Depthwise separable convolution Visual Geometry group-deep gate recurrent neural network, 3.3 Exponential linear unit activation function, Paragraph 3, and should read: “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 (Clevert et al., 2016). 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 new reference replaces 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, which has been removed.
The original article has been updated.
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Keywords: photovoltaic power output prediction, deep learning, depthwise separable convolution, VGG, gate recurrent neural network
Citation: Zhang L, Zhao S, Zhao G, Wang L, Liu B, Na Z, Liu Z, Yu Z and He W (2025) Correction: Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network. Front. Energy Res. 13:1707498. doi: 10.3389/fenrg.2025.1707498
Received: 17 September 2025; Accepted: 10 October 2025;
Published: 24 October 2025.
Edited and reviewed by:
Michael Folsom Toney, University of Colorado Boulder, United StatesCopyright © 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) and the copyright owner(s) 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, MjQ4NDAwMjQ4QHFxLmNvbQ==