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) <doi:10.48550/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 |
| DOI: |
10.32614/CRAN.package.gainML |
| 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 |
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