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Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Overview of attention for article published in Frontiers in Energy Research, January 2023
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About this Attention Score

  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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1 X user

Citations

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7 Dimensions

Readers on

mendeley
7 Mendeley
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Title
Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory
Published in
Frontiers in Energy Research, January 2023
DOI 10.3389/fenrg.2022.1043867
Authors

Xueyi Ai, Shijia Li, Haoxuan Xu

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 14%
Researcher 1 14%
Student > Doctoral Student 1 14%
Unknown 4 57%
Readers by discipline Count As %
Unspecified 1 14%
Computer Science 1 14%
Unknown 5 71%
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 15 January 2023.
All research outputs
#21,509,573
of 26,388,722 outputs
Outputs from Frontiers in Energy Research
#993
of 4,843 outputs
Outputs of similar age
#367,510
of 493,063 outputs
Outputs of similar age from Frontiers in Energy Research
#31
of 385 outputs
Altmetric has tracked 26,388,722 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 4,843 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 57% 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 493,063 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 385 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.