prepareShapeData: Prepare load shape data for load shape clustering

In ConvergenceDA/visdomloadshape: R package for load shape clustering and analysis, extending the visdom package

Description

Offers several options for cleaning and preparing load shape data, including forcing all data to be the same duration, subtracting the daily minimum from each day of load, eliminating load shapes that average less than a minimum power threshold, and down-sampling load data from 24 observations a day to 4.

Usage

prepareShapeData(rawData, forceSameDuration = F, subtractMins = F,
  minPower = NULL, coarseTimePeriods = F, return.mins = F)

Arguments

rawData	data.frame of load shape data, with the first n columns assumed to be metadata, which must include an 'id' column, and the last 24 columns as load shape data.
forceSameDuration	applies a heuristic to preserve only data from ids that have the modal number of days of meter data.
subtractMins	'de-mins' data by subtracting the daily min observation from all daily observations.
minPower	if not null, the function removes load shapes that average less than the provided minPower level.
coarseTimePeriods	down samples data by averaging 24 columns of meter data observation to 4 6-hour averages.
return.mins	whether or not to return the daily min values subtracted by subtractMins

ConvergenceDA/visdomloadshape documentation built on May 8, 2019, 8:34 a.m.