This documents illustrates how to prepare data, how to implement the package, and what the resulting objects are.

For the analysis, this package requires to use at least three turbine datasets (dataframes); one for each of reference turbine, baseline control turbine, and neutral control turbine.

- All dataframes must have at least two data columns, one for timestamp and another for turbine id, the column numbers can be set through
`col.time`

and`col.turb`

. - Other than the two columns, a dataset of a reference turbine must include wind direction, power output, and air density in sequence.
- Other than the two columns, a dataset of a control turbine (both baseline and neutral) must include wind speed and power output in sequence.

To use the package, a user first needs to load the package (attach the package to the current R environment).

`library(gainML)`

Once the package is loaded, a user can (i) simply run a single function `analyze.gain`

or (ii) choose to run multiple functions in sequence (`analyze.gain`

basically runs these functions in sequence).

When using

`analyze.gain`

:`# Analyze Gain in a Single Step point.res <- analyze.gain(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26', ratedPW = 1000, AEP = 300000, pw.freq = pw.freq) point.res$gain.res$gain #Provides the point estimate of gain`

When using multiple functions:

`# Prepare Data data <- arrange.data(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26') # Period 1 Analysis p1.res <- analyze.p1(data$train, data$test, ratedPW = 1000) # Period 2 Analysis p2.res <- analyze.p2(data$per1, data$per2, p1.res$opt.cov) # Quantify gain gain.res <- quantify.gain(p1.res, p2.res, ratedPW = 1000, AEP = 300000, pw.freq = pw.freq) gain.res$gain #Provides the point estimate of gain`

When using

`analyze.gain`

for**free sector analysis**:`free.sec <- list(c(310, 50), c(150, 260)) #Defines the free sectors # Analyze Gain in a Single Step point.res <- analyze.gain(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26', ratedPW = 1000, AEP = 300000, pw.freq = pw.freq, free.sec = free.sec)`

**Note**:`free.sec`

is a list of vectors defining free sectors. Each vector in the list has two scalars: one for starting direction and another for ending direction, ordered clockwise.

For the details about the functions, please refer to the package manual (in a `pdf`

format).

Once the package is loaded, a user needs to run a series of functions as illustrated below.

Full sector analysis:

`# Prepare Data data <- arrange.data(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26') # Period 1 Analysis p1.res <- analyze.p1(data$train, data$test, ratedPW = 1000) # Gain Analysis by Using Bootstrap n.rep <- 10 #Defines the number of replications. interval.res <- bootstrap.gain(df.ref, df.ctrb, df.ctrn, opt.cov = p1.res$opt.cov, n.rep = n.rep, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26', ratedPW = 1000, AEP = 300000, pw.freq = pw.freq, write.path = NULL) sapply(res, function(ls) ls$gain.res$gainCurve) #Provides 10 gain curves sapply(res, function(ls) ls$gain.res$gain) #Provides 10 gain values`

Free sector analysis:

`free.sec <- list(c(310, 50), c(150, 260)) #Defines the free sectors # Prepare Data data <- arrange.data(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26', free.sec = free.sec) # Period 1 Analysis p1.res <- analyze.p1(data$train, data$test, ratedPW = 1000) # Gain Analysis by Using Bootstrap n.rep <- 10 #Defines the number of replications. interval.res <- bootstrap.gain(df.ref, df.ctrb, df.ctrn, opt.cov = p1.res$opt.cov, n.rep = n.rep, free.sec = free.sec, p1.beg = '2014-10-24', p1.end = '2015-10-25', p2.beg = '2015-10-25', p2.end = '2016-10-26', ratedPW = 1000, AEP = 300000, pw.freq = pw.freq, write.path = NULL) sapply(res, function(ls) ls$gain.res$gainCurve) #Provides 10 gain curves sapply(res, function(ls) ls$gain.res$gain) #Provides 10 gain values`

**Note**: The only difference is to define`free.sec`

and set it as an argument when using`arrange.data`

and`bootstrap.gain`

functions.

Period 1 analysis will take a significant amount of time, so its progress will be indicated in the R console.

A user needs to read and store the long term frequency data manually. To see a desired format, please refer to the

`pw.freq`

part in the manual or, in the R console, run`head(pw.freq)`

The analysis outcome can be obtained from the `quantify.gain`

function (the return from `analyze.gain`

and `bootstrap.gain`

will also include this outcome). The outcome includes:

Gain quantification: initial effect, offset, and gain with offset adjustment.

Bin-wise curve: effect curve, offset curve, and gain curve corresponding to each of the above gain quantification, respectively.

Please refer to the package manual for more details.