plr_bootstrap_output {PVplr}

Bootstrap: Resampling from individual Models

Description

This function determines uncertainty of a PLR measurement by sampling results from invididual models. Specify the model you would like to find the uncertainty of, and the function will put the dataframe through the selected model and return the uncertainties of the model's results.

Usage

plr_bootstrap_output(
  df,
  var_list,
  model,
  by = "month",
  fraction = 0.65,
  n = 1000,
  predict_data = NULL,
  np = NA,
  power_var = "power_var",
  time_var = "time_var",
  ref_irrad = 900,
  irrad_range = 10
)

Arguments

df

A dataframe containing pv data.

var_list

A list of the dataframe's standard variable names, obtained from the plr_variable_check output.

model

The model you would like to calculate the uncertainty of. Use "xbx", "xbx+utc", "pvusa", or "6k".

by

String indicating time step count per year for the regression. Use "day", "month", or "year". See plr_weighted_regression.

fraction

The size of each sample relative to the total dataset.

n

Number of samples to take.

predict_data

passed to predict_data in model call. See plr_xbx_model for example.

np

The system's reported name plate power. See plr_6k_model.

power_var

The name of the power variable after being put through a Performance Loss Rate (PLR) determining test. Typically "power_var".

time_var

The name of the time variable after being put through a PLR determining test. Typically "time_var".

ref_irrad

The irradiance value at which to calculate the universal temperature coefficient. Since irradiance is a much stronger influencer on power generation than temperature, it is important to specify a small range of irradiance data from which to estimate the effect of temperature.

irrad_range

The range of the subset used to calculate the universal temperature coefficient. See above.

Value

Returns PLR value and uncertainty calculated with bootstrap of data from power correction models

Examples

# build var_list

var_list <- plr_build_var_list(time_var = "timestamp",
                               power_var = "power",
                               irrad_var = "g_poa",
                               temp_var = "mod_temp",
                               wind_var = NA)
# Clean Data
test_dfc <- plr_cleaning(test_df, var_list, irrad_thresh = 100,
                         low_power_thresh = 0.01, high_power_cutoff = NA)

xbx_mbm_plr_output_uncertainty <- plr_bootstrap_output(test_dfc, var_list,
                                                       model = "xbx", fraction = 0.65,
                                                       n = 10, power_var = 'power_var',
                                                       time_var = 'time_var', ref_irrad = 900,
                                                       irrad_range = 10, by = "month",
                                                       np = NA, pred = NULL)