plr_6k_model: 6k Method for PLR Determination

In PVplr: Performance Loss Rate Analysis Pipeline

6k Method for PLR Determination

Description

This function groups data by the specified time interval and performs a linear regression using the formula: power_var ~ irrad_var/istc * (nameplate_power + a*log(irrad_var/istc) + b*log(irrad_var/istc)^2 + c*(temp_var - tref) + d*(temp_var - tref)*log(irrad_var/istc) + e*(temp_var - tref)*log(irrad_var/istc)^2 + f*(temp_var - tref)^2). Predicted values of irradiance, temperature, and wind speed (if applicable) are added for reference. These values are the lowest daily high irradiance reading (over 300W/m^2), the average temperature over all data, and the average wind speed over all data.

Usage

plr_6k_model(
  df,
  var_list,
  nameplate_power,
  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.
nameplate_power	The rated power capability of the system, in watts.
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 6K modeling

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