plr_bootstrap_output_from_results {PVplr}

Bootstrap: Resample from individual Models

Description

The function samples and bootstraps data that has already been put through a power predictive model. The PLR and Uncertainty are returned in a dataframe.

Usage

plr_bootstrap_output_from_results(
  data,
  power_var,
  time_var,
  weight_var,
  by = "month",
  model,
  fraction = 0.65,
  n = 1000
)

Arguments

data

Result of modeling data with a PLR determining model, i.e. plr_xbx_model, plr_6k_model, etc.

power_var

Variable name of power in the dataframe. Typically power_var

time_var

Variable name of time in the dataframe. Typically time_var

weight_var

Variable name of weightings in the dataframe. Typically sigma

by

String, either "day", "month", or "year". Time over which to perform plr_yoy_regression and plr_weighted_regression.

model

The name of the model the data has been put through. This option is only included for the user's benefit in keeping bootstrap outputs consistent.

fraction

The fractional size of the data to be sampled each time.

n

The number of resamples to take.

Value

Returns PLR value and uncertainty calculated with bootstrap of data going into 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)
                         
# Perform the power predictive modeling step
test_xbx_wbw_res <- plr_xbx_model(test_dfc, var_list, by = "week",
                                  data_cutoff = 30, predict_data = NULL)
                                  
xbx_mbm_plr_result_uncertainty <- plr_bootstrap_output_from_results(test_xbx_wbw_res, 
                                                                    power_var = 'power_var',
                                                                    time_var = 'time_var',
                                                                    weight_var = 'sigma',
                                                                    by = "month", model = 'xbx',
                                                                    fraction = 0.65, n = 10)