# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 1 Introduction
In the current network operation practice, the data provided by the intelligent metering are not fully involved, and the resort to such accurate and updated measured data may be addressed for producing new load profiles of both active and reactive power, more realistically than in the past [2; 3; 5]. These models should be capable of capturing the different behavior of customers by using further information. Such information may not be necessarily too detailed and should not adversely affect the customers' privacy rights by exploiting ad hoc techniques to deal with a vast amount of data [2; 5; 6]. As an example, new LPs could be obtained by filtering field data with geographic information to find correlations with the climate conditions (the use of electricity is different in the northern, colder, areas than in the southern ones), with socio-demographic characteristics (e.g., income, education level, social status), etc. In this paper, an approach for obtaining typical LPs from a database of measured data is proposed. The method is based on well-known clustering techniques used for grouping in clusters similar behaviors of end-users belonging to the same category. The goal is to obtain a set of load consumption shapes that could capture the diversity within the same customer category, potentially homogeneous. The methodology consists of a two-stage procedure that aims at characterizing the single customer, and then it groups different customers into classes. Both stages may be performed with clustering techniques, as detailed in the next sections. The results, i.e., the sets of LPs associated with each customer's category and differentiated between the typical days in the year (i.e., three typical days for each season), can be used for representing existing customers of a given network but also aspire to foresee the behavior of new customers that can be supplied by a new portion of the distribution system. The bullet points below highlight the main advancements compared to the state of the art that can be regarded as the main procedure's point of strength:  
1. capability to deal with large databases from the field (much larger than the ones dealt by almost the totality of methods proposed by the literature);2. output-based approach, i.e., it does not a priori prefer a specific clustering technique but uses the one which provides the better results;
3. accurate association of an LP to a new or a non-monitored existing customer or to a group of customers of the same network considering only few features known by the DSO. From an application point of view, it is worth mentioning that the application of the procedure has produced new and updated LPs, on the basis of recent and large databases gathered from extensive measurement campaigns. The new LPs capture much of the Italian load diversity and can be used to model the load consumption of a modern and developed country provided that a suitable scale factor is used. The more accurate modelling of load diversity reflects on a more accurate representation of load coincidence with an immediate impact on planning studies for expansion studies. The rest of the paper is organized as follows. Section 2 briefly reviews the current state of the research field on the methods for assessing load profiles; in Section 3, the proposed approach is described in detail; Section 4 reports the results of the application of the proposed approach to databases gathered from real measurement campaigns; in Section 5, one example of application of the LPs is proposed for demonstrating the validity of the approach. Finally, the conclusions are reported.