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On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Overview of attention for article published in IEEE Transactions on Sustainable Energy, July 2022
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Mentioned by

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1 X user

Citations

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21 Dimensions

Readers on

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59 Mendeley
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Title
On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems
Published in
IEEE Transactions on Sustainable Energy, July 2022
DOI 10.1109/tste.2022.3194728
Authors

Jiawei Wang, Pierre Pinson, Spyros Chatzivasileiadis, Mathaios Panteli, Goran Strbac, Vladimir Terzija

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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 19%
Researcher 6 10%
Student > Doctoral Student 5 8%
Lecturer 3 5%
Unspecified 3 5%
Other 4 7%
Unknown 27 46%
Readers by discipline Count As %
Engineering 19 32%
Unspecified 3 5%
Computer Science 3 5%
Energy 3 5%
Nursing and Health Professions 1 2%
Other 0 0%
Unknown 30 51%
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 24 August 2022.
All research outputs
#20,673,680
of 25,392,582 outputs
Outputs from IEEE Transactions on Sustainable Energy
#265
of 318 outputs
Outputs of similar age
#320,249
of 432,558 outputs
Outputs of similar age from IEEE Transactions on Sustainable Energy
#6
of 8 outputs
Altmetric has tracked 25,392,582 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 318 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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 432,558 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.