BayesTreePCFit

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

Rui Tuo

Effi Latiffianti

BayesTreePCFit function

<dl><dt>trainX</dt>
<dd>a matrix or dataframe to be used in modelling</dd>
<dt>trainY</dt>
<dd>a numeric or vector as a target</dd>
<dt>testX</dt>
<dd>a matrix or dataframe, to be used in computing the predictions</dd>
<dt>nTree</dt>
<dd>a numeric value specifying number of trees to be constructed in model</dd></dl>

Arguments

Tree based power curve estimate — BayesTreePCFit

<dl>

<dt>trainX</dt>
<dd>a matrix or dataframe to be used in modelling</dd>


<dt>trainY</dt>
<dd>a numeric or vector as a target</dd>


<dt>testX</dt>
<dd>a matrix or dataframe, to be used in computing the predictions</dd>


<dt>nTree</dt>
<dd>a numeric value specifying number of trees to be constructed in model</dd>

</dl>

Tree based power curve estimate

BayesTreePCFit: Tree based power curve estimate

Description

Usage

Value

Arguments

Examples