plr_xbx_utc_model: UTC Method for PLR Determination

In PVplr: Performance Loss Rate Analysis Pipeline

UTC Method for PLR Determination

Description

This function groups data by the specified time interval and performs a linear regression using the formula: power_corr ~ irrad_var - 1. Predicted values of irradiance, temperature, and wind speed (if applicable) are added for reference. The function uses a universal temperature correction, rather than the monthly regression correction done in other PLR determining methods.

Usage

plr_xbx_utc_model(
  df,
  var_list,
  by = "month",
  data_cutoff = 30,
  predict_data = NULL,
  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 output of plr_variable_check.
by	String, either "day", "week", or "month". The time periods over which to group data for regression.
data_cutoff	The number of data points needed to keep a value in the final table. Regressions over less than this number and their data will be discarded.
predict_data	optional; Dataframe; If you have preferred estimations of irradiance, temperature, and wind speed, include them here to skip automatic generation. Format: Irradiance, Temperature, Wind (optional).
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 dataframe of results per passed time scale from XbX with universal temperature correction modeling

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_utc_model(test_dfc, var_list, by = "week",
                                  data_cutoff = 30, predict_data = NULL,
                                  ref_irrad = 900, irrad_range = 10)

PVplr documentation built on Feb. 16, 2023, 9:56 p.m.