Description Usage Arguments Details Value Author(s) See Also
Apply Random Forest to predict AC power of a photovoltaic plant with different combinations of predictors and training set definition methods.
1 2 3 4 5 6 7  rfPredict(test, history, nDays)
predictPac(goal, history, id, nDays, method, point,...)
rfScenario(history, id, nDays, method, typeRes, mc.cores=1,...)
scenarioSet(id, vals)

test 
A 
goal 
A 
history, vals 
A 
id 
Character that identifies the scenario (combination of predictors to be included in the training set). There are 17 scenarios defined in this version of the package. For example, if you wish to use the scenario 3, you have to use 
nDays 
numeric. The prediction is computed using a train set of N days from 
method 
Character. It defines the method used to select the days included in the training set:

typeRes 
Character, if 
point 
point represented by a 
mc.cores 
The number of cores to use, i.e. at most how many child processes will be run simultaneously. Parallelization requires at least two cores. It relies on forking and hence is not available on Windows. 
... 
Additional arguments for 
rfPredict
returns a zoo
object, a time series, with a
column for each quantile prediction, named q1
, q5
, and
q9
. The time index is the same as the test
object. This
function uses Quantile Regression Forests to produce the quantile prediction.
predictPac
returns the prediction produced by rfPredict
with the selected scenario and method, and for the day defined with goal
. It is a zoo
object, a time series, with a column for each quantile prediction, named q1
, q5
, and q9
.
rfScenario
uses predictPac
to construct a time series of
forecasts for every day included in the history
time series. This
function is indicated to asses the performance of the predictions with a
certain scenario. If typeRes = 'power'
the result is a time
series with the same frequency as history
. If typeRes =
'stats'
the result is a daily time series with error statistics and the
clearness index, with a row for each day included in history
.
A zoo
time series.
Marcelo Pinho Almeida and Oscar Perpiñán Lamigueiro
quantregForest
,
predict.quantregForest
,
zoo
,
calcSol
,
local2Solar
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