| calc_features30_consumption {SmartMeterAnalytics} | R Documentation |
Calculates features from 30-min smart meter data
Description
Calculates features from 30-min smart meter data
Usage
calc_features30_consumption(
B,
rowname = NULL,
featsCoarserGranularity = FALSE,
replace_NA_with_defaults = TRUE
)
Arguments
B |
a vector with length 2*24*7 = 336 measurements in one day in seven days a week |
rowname |
the row name of the resulting feature vector |
featsCoarserGranularity |
are the features of finer granularity levels also to be calculated (TRUE/FALSE) |
replace_NA_with_defaults |
replaces missing (NA) or infinite values that may appear during calculation with default values |
Value
a data.frame with the calculated features as columns and a specified rowname, if given
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
References
Hopf, K. (2019). Predictive Analytics for Energy Efficiency and Energy Retailing (1st ed.). Bamberg: University of Bamberg. https://doi.org/10.20378/irbo-54833
Hopf, K., Sodenkamp, M., Kozlovskiy, I., & Staake, T. (2014). Feature extraction and filtering for household classification based on smart electricity meter data. Computer Science-Research and Development, (31) 3, 141–148. https://doi.org/10.1007/s00450-014-0294-4
Hopf, K., Sodenkamp, M., & Staake, T. (2018). Enhancing energy efficiency in the residential sector with smart meter data analytics. Electronic Markets, 28(4). https://doi.org/10.1007/s12525-018-0290-9
Beckel, C., Sadamori, L., Staake, T., & Santini, S. (2014). Revealing household characteristics from smart meter data. Energy, 78, 397–410. https://doi.org/10.1016/j.energy.2014.10.025
Examples
# Create a random time series of 30-minute smart meter data (336 measurements per week)
smd <- runif(n=336, min=0, max=2)
# Calculate the smart meter data features
calc_features30_consumption(smd)