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DSWE (version 1.6.1)

Data Science for Wind Energy

Description

Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) , AMK() - Lee et al. (2015) , tempGP() - Prakash et al. (2022) , ComparePCurve() - Ding et al. (2021) , deltaEnergy() - Latiffianti et al. (2022) , syncSize() - Latiffianti et al. (2022) , imptPower() - Latiffianti et al. (2022) , All other functions - Ding (2019, ISBN:9780429956508).

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Install

install.packages('DSWE')

Monthly Downloads

195

Version

1.6.1

License

MIT + file LICENSE

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Maintainer

Yu Ding

Last Published

July 18th, 2022

Functions in DSWE (1.6.1)

imptPower

Power imputation
predict.tempGP

predict from temporal Gaussian process
funGP

Function comparison using Gaussian Process and Hypothesis testing
deltaEnergy

Energy decomposition for wind turbine performance comparison
data1

Wind Energy data set containing 47,542 data points
data2

Wind Energy data set containing 48,068 data points
updateData

Updating data in a model
updateData.tempGP

Update the data in a tempGP object
KnnUpdate

KNN : Update
tempGP

temporal Gaussian process
syncSize

Data synchronization
SvmPCFit

SVM based power curve modelling
KnnPCFit

KNN : Fit
CovMatch

Covariate Matching
SplinePCFit

Smoothing spline Anova method
ComparePCurve

Power curve comparison
ComputeWeightedDifference

Percentage weighted difference between power curves
KnnPredict

KNN : Predict
BayesTreePCFit

Tree based power curve estimate
AMK

Additive Multiplicative Kernel Regression