# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 2 Load Profiling
The research activity described in this paper is based on two large and recent datasets of measured daily profiles that refer each to a one-year measurement campaign and initially included both over 100,000 monitored end-users. By limiting the study to the customers with both profile and type of contract known, the databases are reduced to 56,034 and 31,790 LV customers, respectively. Therefore, considering the reviewed articles, the databases used in this paper, even reduced, are at the top levels in the ranking and only the activity described in [18] concerns a larger dataset. Moreover, considering the load profiling approaches in the reviewed literature, even if more than one clustering technique is exploited in some papers, the use of one clustering technique only for the load profiling process is common [14; 15; 16; 17; 18; 20; 21; 22; 23; 24; 25]. In [19] and [27], two different clustering techniques were used in a sequential way, while in [26], three different algorithms were compared offline. One of the contributions of this paper is the improvement of the clustering process, which relies on the online comparison of two different clustering techniques (\(k\)-means vs. hierarchical), which are based on different approaches (divisive vs. agglomerative). These two techniques are applied in parallel considering different partition set-up. The overall outcome of the clustering approach depends on the best performances achieved by the two algorithms. The optimal clustering set-up is identified according to the best values of the validity index achieved. Two reviewed papers aimed at enhancing the performances [15] or providing a comparative analysis [14] of acknowledged algorithms. The papers [16; 17; 22; 23] addressed the topic of identifying common patterns among one category of customers only, while more than one category was studied in [20; 24; 25; 26; 27]. In this paper the electric behavior of all the categories of Italian LV customers is studied (i.e., industrial, commercial, agricultural, and residential). As in [18; 19; 21] for the Portuguese power system, the aim of the activities described in this paper is to characterize the typical electricity behavior of the Italian customers by assigning to each category more than one typical profile, thereby improving the state of art of customer segmentation practices. Future works would be devoted to compare the typical load profile of customers of different European Countries. Once a typical pattern is identified for the analyzed profiles in the dataset, the load profiling activity poses the problem of assigning these typical load profiles to customers that do not belong to the analyzed dataset. This stage is not addressed in [14; 15; 16; 17; 19; 21; 22; 24; 27]. In [18], Markov chains were proposed for creating synthetic models of the MV/LV and HV/MV interfaces to be used for analyzing the network at the higher voltage level. In [25], the obtained daily curves were used for highlighting the variability of the household's electricity use. The association of the typical load pattern to new customers was based on external information (e.g., income, education, dwelling size) in [23], and historical data in [20], while in [26], the buildings were assigned to the dominant clusters (i.e., those that occur more frequently, identified by using the entropy computation). In this paper, the association of the most suitable typical load profile to new customers (i.e., those which are not part of the analyzed dataset) is made, considering only features easily available for the DSO (e.g., annual energy, rated power, monthly peak). Thus, it represents a concrete improvement of the state of the art and a step forward in the field of load profiling, since the outcome of the research activity can be exploited in real-life applications, considering the current transitional period and the near with the possible functionalities of advanced metering infrastructures only partially enabled.