Expression of concern: Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network

doi:10.3389/fenrg.2024.1548438

CORRECTION article

Front. Energy Res. , 23 December 2024

Sec. Solar Energy

Volume 12 - 2024 | https://doi.org/10.3389/fenrg.2024.1548438

This article is part of the Research Topic Ensuring the Reliability of Solar Photovoltaics View all 7 articles

Expression of concern: Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network

This article is a correction to:

Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network
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Frontiers Editorial Office*

An Expression of concern on
Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network

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

With this notice, Frontiers states its awareness of concerns regarding the content of the article “Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network” published on 1 August 2024. Our Research Integrity team will conduct an investigation in full accordance with our procedures. The situation will be updated as soon as the investigation is complete.

Citation: Frontiers Editorial Office (2024) Expression of concern: Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network. Front. Energy Res. 12:1548438. doi: 10.3389/fenrg.2024.1548438

Received: 19 December 2024; Accepted: 19 December 2024;
Published: 23 December 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

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Expression of concern: Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network

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