PVplr (version 0.1.2)

plr_xbx_utc_model: 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
)

Value

Returns dataframe of results per passed time scale from XbX with universal temperature correction modeling

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.

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)