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>.

Version: 0.1.0
Depends: R (≥ 3.6.0)
Imports: fields (≥ 9.0), FNN (≥ 1.1), utils, stats
Suggests: knitr, rmarkdown
Published: 2019-06-28
Author: Hoon Hwangbo [aut, cre], Yu Ding [aut], Daniel Cabezon [aut], Texas A&M University [cph], EDP Renewables [cph]
Maintainer: Hoon Hwangbo <hhwangb1 at utk.edu>
License: GPL-3
Copyright: Copyright (c) 2019 Y. Ding, H. Hwangbo, Texas A&M University, D. Cabezon, and EDP Renewables
NeedsCompilation: no
CRAN checks: gainML results

Downloads:

Reference manual: gainML.pdf
Vignettes: Implementation
Package source: gainML_0.1.0.tar.gz
Windows binaries: r-devel: gainML_0.1.0.zip, r-release: gainML_0.1.0.zip, r-oldrel: not available
OS X binaries: r-release: gainML_0.1.0.tgz, r-oldrel: not available

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