plr_yoy_regression {PVplr} | R Documentation |
Year-on-Year Regression
Description
Automatically calculates Performance Loss Rate (PLR) using year on year regression. Note that it needs data from a power predictive model.
Usage
plr_yoy_regression(
data,
power_var,
time_var,
model,
per_year = 12,
return_PLR = TRUE
)
Arguments
data |
Result of a power predictive model |
power_var |
String name of the variable used as power |
time_var |
String name of the variable used as time |
model |
String name of the model the data was passed through |
per_year |
Time step count per year based on model. Typically 12 for MbM, 365 for DbD. |
return_PLR |
boolean; option to return PLR value, rather than the raw regression data. |
Value
Returns PLR value and error evaluated with YoY regression, if return_PLR is false it will return the individual YoY calculations
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)
# Calculate Performance Loss Rate
xbx_wbw_plr <- plr_yoy_regression(test_xbx_wbw_res,
power_var = 'power_var',
time_var = 'time_var',
model = "xbx",
per_year = 52,
return_PLR = TRUE)
[Package PVplr version 0.1.2 Index]