create_dataframe: Generate training or prediction dataframes.

In kW-Labs/nmecr: An implementation of peer-reviewed energy data analysis algorithms for site-specific M&V

Generate training or prediction dataframes.

Description

This function creates a dataframe, combining eload, temp, and additional variable data. create_dataframe is the user friendly wrapper for the aggregate function. By default, it assumes that the input data is aligned to the start of a time period and outputs a dataframe that is also aligned to the start of time periods. In cases where you are using normal weather data (such as TMY) that reports its timestamps at the end of each period, then set shift_normal_weather = TRUE to shift the weather data backwards by one interval to match the timestamp reporting convention for eload.

Usage

create_dataframe(
  eload_data = NULL,
  temp_data = NULL,
  operating_mode_data = NULL,
  additional_independent_variables = NULL,
  additional_variable_aggregation = c(sum, median, mean),
  start_date = NULL,
  end_date = NULL,
  convert_to_data_interval = c("15-min", "Hourly", "Daily", "Monthly"),
  temp_balancepoint = 65,
  shift_normal_weather = FALSE
)

Arguments

eload_data	A dataframe with energy consumption time series. This dataframe should only be energy consumption data and not demand data. Column names: "time" and "eload". Allowed time intervals: less-than 15-min, 15-min, hourly, daily, monthly. The 'time' column must have Date-Time object values.
temp_data	A dataframe with weather time series. Column names: "time" and "temp". Allowed time intervals: 15-min, hourly, daily, monthly. The 'time' column must have Date-Time object values.
additional_independent_variables	An optional dataframe for adding independent variables to the regression. This argument is a replacement for the older 'operating_mode_data' argument. The first column should be titled "time" and all proceeding columns should contain numeric data for each additional independent variable. Allowed time intervals: less-than 15-min, 15-min, hourly, daily
additional_variable_aggregation	A vector with aggregation functions for each of the variables in 'additional_independent_variables'. Usage example: c(sum, median) implies two additional independent variables. The first variable will be summed over the specified data interval and the median of the second variable will be taken over the specified data interval. Permissible aggregation functions: sum, mean, median
start_date	A character string, of the format "mm/dd/yyyy hh:mm", indictating the inclusive start date and time of the intended dataframe
end_date	A character string, of the format "mm/dd/yyyy hh:mm", indictating the inclusive end date and time of the intended dataframe. This parameter can also be used to specify the date for the end of the final reporting period for monthly energy usage data and aggregate other data accordingly. Otherwise, the function will attempt to guess the end date for the final period to aggregate to.
convert_to_data_interval	A character string indicating the time interval to which the dataframe should be aggregated: '15-min', 'Hourly', 'Daily', and 'Monthly'
temp_balancepoint	A numeric indicating the balancepoint for the temp_data dataframe
shift_normal_weather	A logical indicating whether or not to shift the weather data from end of period reporting to beginning of period reporting. This option is set to false by default, but if the user knows that the rest of their data, such as energy use, reports with timestamps at the beginning of each usage period, setting this option to true will align the weather to the beginning of the period as well.

Value

a dataframe with energy consumption data, temperature data and (if supplied) additional variable data at the specified data interval. If only temperature data is supplied, the function will return a dataframe with aggregated temperature data.

kW-Labs/nmecr documentation built on May 6, 2024, 9:28 p.m.