SmartMeterAnalytics (version 1.1.1)

calc_features30_consumption: 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
)

Value

a data.frame with the calculated features as columns and a specified rowname, if given

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

Author

Konstantin Hopf

References

Hopf, K. (2019). Predictive Analytics for Energy Efficiency and Energy Retailing (1st ed.). Bamberg: University of Bamberg. tools:::Rd_expr_doi("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. tools:::Rd_expr_doi("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). tools:::Rd_expr_doi("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. tools:::Rd_expr_doi("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)