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Machine learning for estimation of building energy consumption and performance: a review

Overview of attention for article published in Visualization in Engineering, October 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)

Mentioned by

twitter
5 tweeters

Readers on

mendeley
188 Mendeley
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Title
Machine learning for estimation of building energy consumption and performance: a review
Published in
Visualization in Engineering, October 2018
DOI 10.1186/s40327-018-0064-7
Authors

Saleh Seyedzadeh, Farzad Pour Rahimian, Ivan Glesk, Marc Roper

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 188 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 46 24%
Student > Ph. D. Student 45 24%
Researcher 21 11%
Student > Doctoral Student 12 6%
Student > Bachelor 8 4%
Other 27 14%
Unknown 29 15%
Readers by discipline Count As %
Engineering 71 38%
Energy 31 16%
Computer Science 20 11%
Business, Management and Accounting 5 3%
Design 5 3%
Other 15 8%
Unknown 41 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 October 2018.
All research outputs
#8,101,513
of 15,150,085 outputs
Outputs from Visualization in Engineering
#11
of 36 outputs
Outputs of similar age
#120,537
of 274,753 outputs
Outputs of similar age from Visualization in Engineering
#1
of 1 outputs
Altmetric has tracked 15,150,085 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 36 research outputs from this source. They receive a mean Attention Score of 2.9. This one scored the same or higher as 25 of them.
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 274,753 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them