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A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

Overview of attention for article published in Frontiers in Energy Research, January 2024
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
1 X user

Readers on

mendeley
2 Mendeley
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Title
A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output
Published in
Frontiers in Energy Research, January 2024
DOI 10.3389/fenrg.2023.1338195
Authors

Qipei Zhang, Ningkai Tang, Jixiang Lu, Wei Wang, Lin Wu, Wenteng Kuang

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 50%
Unknown 1 50%
Readers by discipline Count As %
Engineering 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 January 2024.
All research outputs
#21,361,224
of 26,220,821 outputs
Outputs from Frontiers in Energy Research
#972
of 4,795 outputs
Outputs of similar age
#264,424
of 375,157 outputs
Outputs of similar age from Frontiers in Energy Research
#60
of 322 outputs
Altmetric has tracked 26,220,821 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,795 research outputs from this source. They receive a mean Attention Score of 1.6. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 375,157 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 322 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.