CRAN/E | gainML

gainML

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

Installation

About

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) .

Copyright Copyright (c) 2019 Y. Ding, H. Hwangbo, Texas A&M University, D. Cabezon, and EDP Renewables

Key Metrics

Version 0.1.0
R ≥ 3.6.0
Published 2019-06-28 1770 days ago
Needs compilation? no
License GPL-3
CRAN checks gainML results

Downloads

Yesterday 5 +25%
Last 7 days 46 -10%
Last 30 days 154 +3%
Last 90 days 397 -39%
Last 365 days 1.893 -5%

Maintainer

Maintainer

Hoon Hwangbo

hhwangb1@utk.edu

Authors

Hoon Hwangbo

aut / cre

Yu Ding

aut

Daniel Cabezon

aut

Texas A&M University

cph

EDP Renewables

cph

Material

Reference manual
Package source

Vignettes

Implementation

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 3.6.0

Imports

fields ≥ 9.0
FNN ≥ 1.1
utils
stats

Suggests

knitr
rmarkdown