| calc_features15_consumption | Calculates features from 15-min smart meter data |
| calc_features30_consumption | Calculates features from 30-min smart meter data |
| calc_features60_consumption | Calculates features from 15-min smart meter data |
| calc_featuresco_consumption | Calculates consumption features from weekly consumption only |
| calc_featuresda_consumption | Calculates consumption features from daily smart meter data |
| calc_featureshtnt_consumption2 | Calculates consumption features from daily (HT / NT) smart meter data |
| calc_featuresnt_consumption | Calculates consumption features from daily (HT / NT) smart meter data |
| calc_features_daily_multipleTS | Calculates feature from multiple time series data vectors |
| calc_features_weather | Calculates features from one environmental time-series variable and smart meter data |
| encode_p_val_stars | Encodes p-values with a star rating according to the Significance code: |
| features_all_subsets | Creates a set of all combinations of features |
| getDay_ISO8601_week | Retrieves the date of the monday in a ISO8601 week-string |
| getDay_US_week | Retrieves the date of the monday in a US week-string (as implemented by R as.Date) |
| interpolate_missingReadings | Interpolate missing readings |
| naInf_omit | Removes the rows with NA or Inf values |
| occupancy_cluster | Determines two clusters of high and low consumption times (e.g., non-ocupancy during holidays) |
| prepareFeatureSet | Compiles a list of features from energy consumption data |
| remove_empty_features | Removes variables with no necessary information from a data.frame |
| replaceNAsFeatures | Replaces NA values with a given ones |
| smote | Synthetic minority oversampling (SMOTE) |