Expression of concern: Short-time photovoltaic output prediction method based on depthwise separable convolution visual geometry group-deep gate recurrent neural network
Year: 2024
Type: article
Abstract: Expression of Concern: Short-time Photovoltaic output prediction method based on Depthwise Separable Convolution Visual Geometry Group-Deep Gate Recurrent Neural NetworkAn expression of concern on Zhang L, Zhao S, Zhao G, Wang L, Liu B, Na Z, Liu Z, Yu Z and He W (2024) Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network. Front. Energy Res. 12:1447116. doi: 10.3389/fenrg.2024.1447116With 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 1st 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.
Source: Frontiers in Energy Research
Author: Frontiers Editorial Office
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,795
OpenAlex ID: W4405703457