Package: gainML 0.1.0

gainML: Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators

Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arxiv:1906.05776>.

Authors:Hoon Hwangbo [aut, cre], Yu Ding [aut], Daniel Cabezon [aut], Texas A&M University [cph], EDP Renewables [cph]

gainML_0.1.0.tar.gz
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gainML_0.1.0.tgz(r-4.4-any)gainML_0.1.0.tgz(r-4.3-any)
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gainML_0.1.0.tgz(r-4.4-emscripten)gainML_0.1.0.tgz(r-4.3-emscripten)
gainML.pdf |gainML.html✨
gainML/json (API)

# Install 'gainML' in R:
install.packages('gainML', repos = c('https://hhwangbo.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/hhwangbo/gainml/issues

Datasets:
  • pw.freq - Long-Term Frequency of Power Output
  • wtg - Wind turbine operational data

On CRAN:

3.70 score 2 scripts 113 downloads 6 exports 7 dependencies

Last updated 5 years agofrom:2852835bba. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:analyze.gainanalyze.p1analyze.p2arrange.databootstrap.gainquantify.gain

Dependencies:dotCall64fieldsFNNmapsRcppspamviridisLite

gainML: Preparation and Implementation

Hoon Hwangbo

Rendered fromImplementation.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2019-06-25
Started: 2019-06-25