| Type: | Package |
| Title: | Methods for Smart Meter Data Analysis |
| Version: | 1.1.1 |
| Date: | 2025-04-18 |
| Description: | Methods for analysis of energy consumption data (electricity, gas, water) at different data measurement intervals. The package provides feature extraction methods and algorithms to prepare data for data mining and machine learning applications. Deatiled descriptions of the methods and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> "Enhancing energy efficiency in the residential sector with smart meter data analytics". |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | plyr, futile.logger, FNN, stinepack, zoo |
| Suggests: | stringr, knitr, rmarkdown, ROCR, randomForest, caret, dplyr |
| NeedsCompilation: | no |
| Packaged: | 2025-04-19 21:08:11 UTC; khopf |
| Author: | Konstantin Hopf 
[aut, cre],
Andreas Weigert [ctb],
Ilya Kozlovskiy [ctb],
Thorsten Staake
[ctb] |
| Maintainer: | Konstantin Hopf <konstantin.hopf@uni-bamberg.de> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-19 21:32:04 UTC |
Calculates features from 15-min smart meter data
Description
Calculates features from 15-min smart meter data
Usage
calc_features15_consumption(
B,
rowname = NULL,
featsCoarserGranularity = FALSE,
replace_NA_with_defaults = TRUE
)
Arguments
B |
a vector with length 4*24*7 = 672 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. 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. 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). doi:10.1007/s12525-018-0290-9
Examples
# Create a random time series of 15-minute smart meter data (672 measurements per week)
smd <- runif(n=672, min=0, max=2)
# Calculate the smart meter data features
calc_features15_consumption(smd)
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. 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. 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). 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. 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)
Calculates features from 15-min smart meter data
Description
Calculates features from 15-min smart meter data
Usage
calc_features60_consumption(B, rowname = NULL, replace_NA_with_defaults = TRUE)
Arguments
B |
a vector with length 24*7 = 168 measurements in one day in seven days a week |
rowname |
the row name of the resulting feature vector |
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 the row name of the resulting feature vector
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
Examples
# Create a random time series of 60-minute smart meter data (168 measurements per week)
smd <- runif(n=168, min=0, max=2)
# Calculate the smart meter data features
calc_features60_consumption(smd)
Calculates feature from multiple time series data vectors
Description
This function is intended to compute features for daily consumption data from electricity, gas, and water consumption time series data.
Usage
calc_features_daily_multipleTS(
el = NULL,
gas = NULL,
wa = NULL,
rowname = NULL,
cor.useNA = "complete.obs"
)
Arguments
el |
electricity consumption |
gas |
gas consumption |
wa |
water consumption |
rowname |
the name of the consumer (e.g., a household ID in a study database) |
cor.useNA |
an optional character string for the cor function, specifying a method for computing covariances in the presence of missing values. |
Value
a data frame with feature values as columns, named by 'rowname'
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. doi:10.20378/irbo-54833
Calculates features from one environmental time-series variable and smart meter data
Description
Calculates features from one environmental time-series variable and smart meter data
Usage
calc_features_weather(SMD, WEATHER, rowname = NULL)
Arguments
SMD |
the load trace for one week (vector with 672 or 336 elements) |
WEATHER |
weather observations (e.g. temperature) in 30-minute readings (vector with 336 elements) |
rowname |
the row name of the current data point |
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de, Ilya Kozlovslkiy
References
Hopf, K. (2019). Predictive Analytics for Energy Efficiency and Energy Retailing (1st ed.). Bamberg: University of Bamberg. 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. 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). doi:10.1007/s12525-018-0290-9
Calculates consumption features from weekly consumption only
Description
Calculates consumption features from weekly consumption only
Usage
calc_featuresco_consumption(B, rowname = NULL)
Arguments
B |
a vector of any length with measurements |
rowname |
the row name of the resulting feature vector |
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. 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. 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). doi:10.1007/s12525-018-0290-9
Calculates consumption features from daily smart meter data
Description
Calculates consumption features from daily smart meter data
Usage
calc_featuresda_consumption(
B,
rowname = NULL,
featsCoarserGranularity = FALSE,
replace_NA_with_defaults = TRUE
)
Arguments
B |
a vector with length 7 measurements |
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. doi:10.20378/irbo-54833
Calculates consumption features from daily (HT / NT) smart meter data
Description
The division in HT / NT is done from the input smart meter data
Usage
calc_featureshtnt_consumption2(
HTCons,
NTCons,
rowname = NULL,
featsCoarserGranularity = FALSE
)
Arguments
HTCons |
a vector with 7 measurements for HT consumption in one week (beginning with monday) |
NTCons |
a vector with 7 measurements for NT consumption in one week (beginning with monday) |
rowname |
the row name of the resulting feature vector |
featsCoarserGranularity |
are the features of finer granularity levels also to be calculated (T/FALSE) |
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. doi:10.20378/irbo-54833
Calculates consumption features from daily (HT / NT) smart meter data
Description
The division in HT / NT is done from the input smart meter data
Usage
calc_featuresnt_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 |
an optional boolean argument specifying if missing values will be replaced with standard values (i.e., zero values) |
Details
HT consumption is during the time 07:00-22:00
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. 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. 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). doi:10.1007/s12525-018-0290-9
Encodes p-values with a star rating according to the Significance code:
Description
'.' for p-value < 0.1, '*' for < 0.05, '**' for < 0.01, '***' for < 0.001
Usage
encode_p_val_stars(pval)
Arguments
pval |
the p-value |
Value
character with the encoding
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
Creates a set of all combinations of features
Description
Creates a set of all combinations of features
Usage
features_all_subsets(set)
Arguments
set |
vector of available festures that are premutated |
Value
a list of subsets of the input vector
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de, Ilya Kozlovskiy
Examples
features_all_subsets(c("A", "B", "C"))
Retrieves the date of the monday in a ISO8601 week-string
Description
Example date formats defined by ISO 8601: * Single days are written in yyy-mm-dd (y: year, m: month, d: day); e.g., 2016-07-19 * Weeks are written in yyyy-Www; e.g., 2016-W29
Usage
getDay_ISO8601_week(
theweek,
day = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
)
Arguments
theweek |
the string with the week name |
day |
the weekday that shall be returned |
Details
The function uses format und as.Date internally and can therefore not handle ISO8601 week formats. Therefore, a workaround is implemented that can lead to suspicious behavior in future versions
Value
the date of the weekday in the given week
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
Retrieves the date of the monday in a US week-string (as implemented by R as.Date)
Description
According to date formats defined by ISO 8601: * Single days are written in yyy-mm-dd (y: year, m: month, d: day); e.g., 2016-07-19 * Weeks are written in yyyy-WUww; e.g., 2016-WU29 (typically with the first Sunday of the year as day 1 of week 1)
Usage
getDay_US_week(
theweek,
day = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
)
Arguments
theweek |
the string with the week name |
day |
the weekday that shall be returned |
Value
the date of the weekday in the given week
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
Interpolate missing readings
Description
Interpolate missing readings
Usage
interpolate_missingReadings(timeseries, option = "linear", ...)
Arguments
timeseries |
Numeric Vector ( |
option |
Algorithm to be used. Accepts the following input: |
... |
Additional parameters to be passed through to approx or spline interpolation functions |
Details
Missing values get replaced by values of a approx, spline or stinterp interpolation.
Value
Vector (vector) or Time Series (ts) object (dependent on given input at parameter x)
Author(s)
The implementation is adopted from the package imputeTS, function na.interpolate (https://github.com/SteffenMoritz/imputeTS/blob/master/R/na.interpolation.R)
Removes the rows with NA or Inf values
Description
Cleans up a data.frame or matrix which is useful for cases wehere you need complete datasets
Usage
naInf_omit(V)
Arguments
V |
A data.frame or matrix which has to be cleaned |
Value
A cleaned version of data.frame or matrix
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
See Also
replaceNAsFeatures, remove_empty_features
Determines two clusters of high and low consumption times (e.g., non-ocupancy during holidays)
Description
Determines two clusters of high and low consumption times (e.g., non-ocupancy during holidays)
Usage
occupancy_cluster(consumption, n_days_check = 4, sds_between_clusters = 1.5)
Arguments
consumption |
the consumption time series |
n_days_check |
number of consecutive days that should be considered as a minimal cluster |
sds_between_clusters |
the multiples of standatd deviation that must be at least between the cluster centers (decimal number) |
Value
list with cluster assignments and the k-Means clustering model
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
Compiles a list of features from energy consumption data
Description
Returns a vector of feature names that can be calculated by methods in the *SmartMeterAnalytics* package obtains the feature set according
Usage
prepareFeatureSet(
features.granularity = NA,
features.w_adj = FALSE,
features.anonymized = FALSE,
features.categorical = FALSE,
features.geo = "osm-v1",
features.temperature = TRUE,
features.weather = TRUE,
features.neighborhood = FALSE
)
Arguments
features.granularity |
Character: The granularity of the input data, either "15-min" (only 15-min features), "30-min" (only 30-minute features), "all_30min_to_week" (all features on daily, weekly, hourly, ..., up to 30-min data), "all_15_week" (all up to 15-min dara), "week" (only the consumption of one week as a single feature). |
features.w_adj |
Boolean: are the features to be weather adjusted with DiD-Class (NOT IMPLEMENTED YET!) |
features.anonymized |
Boolean: are anonymized geographic features used (NOT IMPLEMENTED YET!) |
features.categorical |
Boolean: use categorical features additionally (if only numeric features are used) |
features.geo |
Character: Version of the geographic feature set (either "none", "osm-v1", "osm-v2") |
features.temperature |
Boolean, if features for the temperature should be included |
features.weather |
Boolean, if other weather features should be included |
features.neighborhood |
Boolean, if features for the neighborhood should be included |
Value
Character vector
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. 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. 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). 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. doi:10.1016/j.energy.2014.10.025
Removes variables with no necessary information from a data.frame
Description
Removes variable names from a list of variables that contain only, or a large portion of, NA values or have zero bandwidth (if they are numeric) and returns the variable names.
Usage
remove_empty_features(
all.features,
dataset,
percentage_NA_allowed = NA,
bandwidth = (.Machine$double.eps^0.5),
verbose = FALSE
)
Arguments
all.features |
a character vector with all column names of |
dataset |
the dataset as a data.frame |
percentage_NA_allowed |
the percentage of missing values per vector that should be allowed without removing the feature. All features with NA values that are higher than this level are excluded. |
bandwidth |
The length of the interval that values of variable must exceed to be not
removed. By default, half of |
verbose |
boolean if debug messages should be printed when a variable is removed from the list (uses flog.debug) |
Details
The function checks all given column names for the portion of NA values.
If the number of NA of Inf exceeds percentage_NA_allowed,
the column name is removed from the variable set. Besides, all numeric
variables are checked if they have almost zero bandwidth, are removed.
Value
a vector of variable names that are not considered as empty
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
See Also
naInf_omit, replaceNAsFeatures
Replaces NA values with a given ones
Description
Taks a data.frame and replaces all NA values with a certain value.
Usage
replaceNAsFeatures(indata, features, replacement = 0)
Arguments
indata |
|
features |
a vector of variable names (must be colum names of |
replacement |
the alternative value, NA values should be replaced with, zero by default |
Value
the modified data.frame with replaced values
Author(s)
Konstantin Hopf konstantin.hopf@uni-bamberg.de
See Also
naInf_omit, remove_empty_features
Synthetic minority oversampling (SMOTE)
Description
Performs oversampling by creating new instances.
Usage
smote(
Variables,
Classes,
subset_use = NULL,
k = 5,
use_nearest = TRUE,
proportions = 0.9,
equalise_with_undersampling = FALSE,
safe = FALSE
)
Arguments
Variables |
the data.frame of independent variables that should be used to create new instances |
Classes |
the class labels in the prediction problem |
subset_use |
a specific subset only is used for the oversampling. If NULL, everything is used. |
k |
the number of neigbours for generation |
use_nearest |
should only the nearest neighbours be used? (very slow) |
proportions |
to which proportion (of the biggest class) should the classes be equalized |
equalise_with_undersampling |
should additional undersampling be performed? |
safe |
should a safe version of SMOTE be used? |
Details
SMOTE is used to generate synthetic datapoints of a smaller class, for example to overcome the problem of imbalanced classes in classification.
Value
a list containing new independent variables data.frame and new class labels
Author(s)
Ilya Kozlovskiy, Konstantin Hopf konstantin.hopf@uni-bamberg.de