Package: DSWE 1.8.0

Yu Ding

DSWE: Data Science for Wind Energy

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) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, All other functions - Ding (2019, ISBN:9780429956508).

Authors:Nitesh Kumar [aut], Abhinav Prakash [aut], Yu Ding [aut, cre], Effi Latiffianti [ctb, cph], Ahmadreza Chokhachian [ctb, cph]

DSWE_1.8.0.tar.gz
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DSWE_1.8.0.tgz(r-4.4-x86_64)DSWE_1.8.0.tgz(r-4.4-arm64)DSWE_1.8.0.tgz(r-4.3-x86_64)DSWE_1.8.0.tgz(r-4.3-arm64)
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DSWE_1.8.0.tgz(r-4.4-emscripten)DSWE_1.8.0.tgz(r-4.3-emscripten)
DSWE.pdf |DSWE.html✨
DSWE/json (API)

# Install 'DSWE' in R:

install.packages('DSWE', repos = c('https://tamu-aml.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tamu-aml/dswe-package/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

data1 - Wind Energy data set containing 47,542 data points
data2 - Wind Energy data set containing 48,068 data points

On CRAN:

16 exports 11 stars 1.97 score 91 dependencies 1 mentions 307 downloads

Last updated 8 months agofrom:d370677c11. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 13 2024
R-4.5-win-x86_64	OK	Sep 13 2024
R-4.5-linux-x86_64	OK	Sep 13 2024
R-4.4-win-x86_64	OK	Sep 13 2024
R-4.4-mac-x86_64	OK	Sep 13 2024
R-4.4-mac-aarch64	OK	Sep 13 2024
R-4.3-win-x86_64	OK	Sep 13 2024
R-4.3-mac-x86_64	OK	Sep 13 2024
R-4.3-mac-aarch64	OK	Sep 13 2024

Exports:AMK ComparePCurve ComputeWeightedDifference CovMatch deltaEnergy funGP imptPower KnnPCFit KnnPredict KnnUpdate SplinePCFit SvmPCFit syncSize tempGP updateData XgbPCFit

Dependencies:askpass base64enc BH bslib cachem class cli colorspace cpp11 crosstalk curl data.table digest dotCall64 dplyr e1071 evaluate fansi farver fastmap fields FNN fontawesome fs generics ggplot2 glue GpGp GPvecchia gss gtable highr htmltools htmlwidgets httr isoband jquerylib jsonlite kernlab KernSmooth knitr labeling later lattice lazyeval lifecycle magrittr maps MASS Matrix matrixStats memoise mgcv mime mixtools munsell nlme openssl pillar pkgconfig plotly promises proxy purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo rlang rmarkdown sass scales segmented spam sparseinv stringi stringr survival sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun xgboost yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Additive Multiplicative Kernel Regression	AMK
Power curve comparison	ComparePCurve
Percentage weighted difference between power curves	ComputeWeightedDifference
Covariate Matching	CovMatch
Wind Energy data set containing 47,542 data points	data1
Wind Energy data set containing 48,068 data points	data2
Energy decomposition for wind turbine performance comparison	deltaEnergy
Function comparison using Gaussian Process and Hypothesis testing	funGP
Power imputation	imptPower
KNN : Fit	KnnPCFit
KNN : Predict	KnnPredict
KNN : Update	KnnUpdate
predict from temporal Gaussian process	predict.tempGP
Smoothing spline Anova method	SplinePCFit
SVM based power curve modelling	SvmPCFit
Data synchronization	syncSize
temporal Gaussian process	tempGP
Updating data in a model	updateData
Update the data in a tempGP object	updateData.tempGP
xgboost based power curve modelling	XgbPCFit