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DSWE (version 1.8.2)

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.packages('DSWE')

Monthly Downloads

246

Version

1.8.2

License

MIT + file LICENSE

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Last Published

February 17th, 2024

Functions in DSWE (1.8.2)

syncSize

Data synchronization
tempGP

temporal Gaussian process
predict.tempGP

predict from temporal Gaussian process
imptPower

Power imputation
KnnUpdate

KNN : Update
ComputeWeightedDifference

Percentage weighted difference between power curves
KnnPCFit

KNN : Fit
XgbPCFit

xgboost based power curve modelling
AMK

Additive Multiplicative Kernel Regression
KnnPredict

KNN : Predict
CovMatch

Covariate Matching
SvmPCFit

SVM based power curve modelling
SplinePCFit

Smoothing spline Anova method
ComparePCurve

Power curve comparison
updateData

Updating data in a model
deltaEnergy

Energy decomposition for wind turbine performance comparison
data1

Wind Energy data set containing 47,542 data points
funGP

Function comparison using Gaussian Process and Hypothesis testing
updateData.tempGP

Update the data in a tempGP object
data2

Wind Energy data set containing 48,068 data points