## 8 Conclusions  
The emission of harmonic currents in public LV networks is mainly influenced by the consumer and generating configuration. Especially the harmonic emission of certain consumer configurations, such as commercial areas, is strongly related to the typical usage of the connected equipment. The analysis of weekly and daily time series of harmonic currents confirms that the three different day types, Mondays to Fridays, Saturdays, and Sundays and Holidays, are represented. The harmonic emission behaviour for these day types is very consistent and differs according to the typical German consumer configurations residential, offices, commercial, and mixed areas.  
The harmonic emission behaviour was used for the development of a classification system in order to identify a probable consumer configuration based on the measurement of their respective harmonic currents, especially the 3 rd harmonic current. A binary decision tree using support vector machines was used for the classification system, which requires training for the actual application. The features to distinguish between the different classes were introduced for each of the four SVMs. The measured emission profiles were grouped accordingly to the predefined classes. The performance evaluation was highlighted for 360 emission profiles, which were split into 50% for the training set and 50% for the test set. The results showed an average classification rate of at least 95%. The random split into training and test samples was repeated one hundred times in order to evaluate the robustness of the classification, and revealed similar classification rates for the training and test processes.  
The emission profiles used for the training process strongly influenced the classification results. A measure of misclassification was introduced in order to enable the identification of misclassified emission profiles by the user of the classification system. It can be used as an indicator for emission profiles that differ strongly from the assigned profile class respective to their associated training samples. A manual review of misclassified profiles enables the user to identify unusual emission behaviour of specific measurement sites, i.e., anomaly detection.  
The automatic identification of consumer configurations based on their harmonic emission is a first contribution towards more automated analysis of large data quantities and to keeping the routine assessment of PQ levels efficient.  
Conceptualization, M.D. and I.Y.-H.G.; methodology, M.D. and I.Y.-H.G.; software, M.D.; validation, M.D.; formal analysis, M.D.; investigation, M.D.; resources, P.S.; data curation, M.D. and J.M.; writing--original draft preparation, M.D.; writing--review and editing, M.D., I.Y.-H.G. and J.M.; visualization, M.D; project administration, P.S.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft--DFG), grant number SCHE 571/8-2.  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from measurement campaigns with two German distribution system operators and are available with their permission.  
The authors declare no conflict of interest.