MCP-class: MCP Class

In btaute/powerResource: Tools To Analyze And Extrapolate Solar Resource Data

Description

This class contains all of the inputs and results related to an MCP. An "MCP" is an acronym for Measure Correlate Predict, which is a common approach to correct the bias between measured data and longterm, modeled data. The methodology is to fit a linear regression of the concurrent measured and modeled data and then use that linear fit to predict future data.

Arguments

megaframe	A dataframe that contains all of the measured and modeled data in a specific, tidy format
minimum_required_hours_for_daily_inclusion	The number of hourly data points required to include a day in the analysis
model_coefficient_is_0	Should the linear regression force the intercept to zero?

Fields

results

A dataframe containing the results of an MCP run

daily_results_table

A dataframe of the results formatted to show daily timestep results more clearly

hourly_results_table

A dataframe of the results formatted to show hourly timestep results more clearly

Methods

get_daily_results_table()

Get Results of daily-timestep MCP runs, formatted in an easy-to-read table format.

get_hourly_results_table()

Get Results of hourly-timestep MCP runs, formatted in an easy-to-read table format.

get_results()

Get Results of all MCP runs

initialize( megaframe = megaframe, minimum_required_hours_for_daily_inclusion = 6, model_coefficient_is_0 = FALSE )

Creates an instance of the class. Call with MCP().

run()

Run an MCP for all timesteps (daily and hourly) and sensors (ghi, ws, temp).

Examples

## Not run: 
my_mcp <- MCP(megaframe)
my_mcp$run()
my_mcp$get_results()

## End(Not run)

btaute/powerResource documentation built on Sept. 4, 2020, 4:36 p.m.