KnnPredict

The function can be used to make prediction on test data using trained model

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).

Yu Ding

DSWE

Data Science for Wind Energy

Nitesh Kumar

Abhinav Prakash

Effi Latiffianti

Ahmadreza Chokhachian

KnnPredict function

<dl><dt>knnMdl</dt>
<dd>a list containing:<ul>
<li>knnMdl$data - The data set provided by user</li>
<li>knnMdl$xCol - The column number of features provided by user or the best subset column number</li>
<li>knnMdl$yCol - The column number of target provided by user</li>
<li>knn$bestK - The best k nearest neighbor calculated using the function KnnFit</li>
</ul></dd>
<dt>testData</dt>
<dd>a data frame or matrix, to compute the predictions</dd></dl>

Arguments

KNN : Predict — KnnPredict

<dl>

<dt>knnMdl</dt>
<dd>a list containing:<ul>
<li>knnMdl$data - The data set provided by user</li>
<li>knnMdl$xCol - The column number of features provided by user or the best subset column number</li>
<li>knnMdl$yCol - The column number of target provided by user</li>
<li>knn$bestK - The best k nearest neighbor calculated using the function KnnFit</li>
</ul></dd>


<dt>testData</dt>
<dd>a data frame or matrix, to compute the predictions</dd>

</dl>

KnnPredict: KNN : Predict

Description

Usage

Value

Arguments

Examples