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

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.

Publication Details

Journal
Frontiers in Energy Research
Published
2024-12-23
DOI
https://doi.org/10.3389/fenrg.2024.1548438
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Frontiers in Energy Research
Industrial Vision Systems and Defect Detection
article

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

article en

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.

Frontiers in Energy ResearchVol. 12
Affordable and clean energy
Openalex Percentile: Top 65%
Industrial Vision Systems and Defect Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.