Package: SmartMeterAnalytics 1.0.3

SmartMeterAnalytics: Methods for Smart Meter Data Analysis

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".

Authors:Konstantin Hopf [aut, cre], Andreas Weigert [ctb], Ilya Kozlovskiy [ctb], Thorsten Staake [ctb]

SmartMeterAnalytics_1.0.3.tar.gz
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SmartMeterAnalytics.pdf |SmartMeterAnalytics.html✨
SmartMeterAnalytics/json (API)

# Install 'SmartMeterAnalytics' in R:
install.packages('SmartMeterAnalytics', repos = c('https://hopfkons.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 186 downloads 17 exports 10 dependencies

Last updated 4 years agofrom:0addbe5ed4. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winNOTENov 14 2024
R-4.5-linuxNOTENov 14 2024
R-4.4-winOKNov 14 2024
R-4.4-macOKNov 14 2024
R-4.3-winOKNov 14 2024
R-4.3-macOKNov 14 2024

Exports:calc_features_daily_multipleTScalc_features_weathercalc_features15_consumptioncalc_features30_consumptioncalc_features60_consumptioncalc_featuresco_consumptioncalc_featuresda_consumptioncalc_featureshtnt_consumption2calc_featuresnt_consumptionencode_p_val_starsfeatures_all_subsetsnaInf_omitoccupancy_clusterprepareFeatureSetremove_empty_featuresreplaceNAsFeaturessmote

Dependencies:FNNformatRfutile.loggerfutile.optionslambda.rlatticeplyrRcppstinepackzoo

Readme and manuals

Help Manual

Help pageTopics
Calculates feature from multiple time series data vectorscalc_features_daily_multipleTS
Calculates features from one environmental time-series variable and smart meter datacalc_features_weather
Calculates features from 15-min smart meter datacalc_features15_consumption
Calculates features from 30-min smart meter datacalc_features30_consumption
Calculates features from 15-min smart meter datacalc_features60_consumption
Calculates consumption features from weekly consumption onlycalc_featuresco_consumption
Calculates consumption features from daily smart meter datacalc_featuresda_consumption
Calculates consumption features from daily (HT / NT) smart meter datacalc_featureshtnt_consumption2
Calculates consumption features from daily (HT / NT) smart meter datacalc_featuresnt_consumption
Encodes p-values with a star rating according to the Significance code:encode_p_val_stars
Creates a set of all combinations of featuresfeatures_all_subsets
Retrieves the date of the monday in a ISO8601 week-stringgetDay_ISO8601_week
Retrieves the date of the monday in a US week-string (as implemented by R as.Date)getDay_US_week
Interpolate missing readingsinterpolate_missingReadings
Removes the rows with NA or Inf valuesnaInf_omit
Determines two clusters of high and low consumption times (e.g., non-ocupancy during holidays)occupancy_cluster
Compiles a list of features from energy consumption dataprepareFeatureSet
Removes variables with no necessary information from a data.frameremove_empty_features
Replaces NA values with a given onesreplaceNAsFeatures
Synthetic minority oversampling (SMOTE)smote