plr_pvusa_model: PVUSA Method for PLR Determination

In PVplr: Performance Loss Rate Analysis Pipeline

PVUSA Method for PLR Determination

Description

This function groups data by the specified time interval and performs a linear regression using the formula: P = G_{POA} * (β_{0} + β_{1} G + β_{2} T_{amb} + β_{3} W). Predicted values of irradiance, temperature, and wind speed (if applicable) are added for reference. These values are the lowest daily high irradiance reading (over 300), the average temperature over all data, and the average wind speed over all data.

Usage

plr_pvusa_model(
  df,
  var_list,
  by = "month",
  data_cutoff = 30,
  predict_data = NULL
)

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).

Value

Returns dataframe of results per passed time scale from PVUSA 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_pvusa_model(test_dfc, var_list, by = "week",
                                  data_cutoff = 30, predict_data = NULL)

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