## 1 Introduction  
The importance of power quality (PQ) in distribution networks continuously increases. One of the reasons is the integration of new device technologies on a large scale, like photovoltaic inverters, the transition from incandescent to LED lamps, and the integration of battery electric vehicles and smart distribution systems. This may result in an increased deterioration of PQ, which could in return affect other electronic devices, in particular if their sensitivity to a particular PQ phenomenon (e.g., harmonic distortion) is high (low immunity).  
Increased levels of harmonic distortion can, for example, cause audible noise and additional losses in network elements and devices, resulting in additional thermal stress, which can result in reduced lifetime. Harmonic can also result in interference in communication, increased neutral conductor loading in LV networks, and reversible or irreversible malfunction of devices [1; 2; 3].  
The prices of PQ monitoring equipment have continuously decreased during the last decades. Especially the integration of intelligent electronic devices (IED), like energy meters, enables extremely cost-efficient solutions [4]. Due to this trend, network operators have intensified their PQ measurement activities and the number of monitored sites rapidly increases. Consequently, the amount of PQ measurement data increases and its efficient management and analysis becomes a challenge of high complexity [5]. As also identified by the CIRED/CIGRE working group C4.112 \"Guidelines for Power quality monitoring\" [6], efficient methods and algorithms to analyze these large amounts of data are a key issue for future smart grids. Efficient and automated methods for processing large amounts of data are essential, but rarely exist at present.  
Harmonic emission in public LV networks is mainly determined by the type and number of connected equipment with power electronic interfaces and depends on the behavior of the connected consumers/users (e.g., office hours) and the amount of generation (e.g., time of the day). Subsequently, PQ levels in public LV networks will vary over time and allow for the definition of distinctive emission profiles for harmonic currents. The different characteristics of these harmonic emission profiles can be used for the construction of a classification system for the identification of the probable consumer configuration based on the measurements of their harmonic currents.  
Section 2 introduces the impact factors for PQ in public LV networks, as well as the typical variations within their time series. The German measurement campaign, which is the basis for the measurement data, is presented in the following section, highlighting the consumer configurations of the 40 measurement sites and the analyzed harmonic currents. The automatic generation of characteristic classes of typical harmonic emission profiles using measurements is explained in Section 4. The design of a classification system using a binary decision tree in combination with support vector machines is part of Section 5. The performance of the training and the testing process of the classification system is evaluated in Section 6. The last section introduces a measure of misclassification, which allows the user of the classification system to identify emission profiles that are incorrectly assigned to the defined classes of emission profiles.