# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 4 Results and Discussion
### Databases  
Two databases (DBs) were used in this paper. They were constituted by the consumption patterns of tens of thousands of Italian customers acquired with a sampling rate of 15 min for two measurement campaigns conducted in different years (i.e., between the years 2013 and 2014, hereinafter referred to as DB2013, and in 2017, referred as DB2017), and by two registers with some information about the customers. For each customer included in the registers, several features were known, not all significant for this study:  
* type of contract (i.e., main or secondary residence contracts, or \"other usage\" for customers different from residential);
* the economic sector, useful for distinguishing customers different from residential into the traditional categories (i.e., industrial, commercial, and agricultural);
* prosumer or not, and in the case of yes, the rated power of the generation plant;
* rated power (e.g., the typical rated power of residential customers is 3 kW);
* the phase of the connection (only for the 2017 database);
* geographic information (i.e., region, province, municipalities), useful for associating the climatic zone (identified by one of the first six capital letters (A-F), univocally identified by the municipality [39]);
* monthly energy consumption (kWh/month), useful for assessing the yearly energy consumption (kWh/year).  
It is worth noticing that the measured consumption profiles and the register of the customers included in the databases were not homogeneous, even if they referred to the same year's dataset. Furthermore, even for the customers included both in the registers and in the profiles' database, the data were not always reliable. For instance, in many cases, several monthly consumption patterns were missing for a specific customer. Thus, his/her yearly consumption cannot be exactly calculated but, in some cases, only reasonably estimated. Therefore, many customers were excluded from the study due to incompleteness (i.e., too many null samples or no commodity sector/type of contract identification). The empirical rules defined according to the setting parameters reported in Table 1 were applied to pre-process the data and limit the study to sufficiently reliable and accurate data. After the pre-selection, the share of the final groups of customers established to be valid for the next analysis was assessed. Moreover, further selection operated during the second stage of the classification process for eliminating the outliers reduces these shares (Table 2). In Table 3 the maximum number of valid customers of each category and the resulting maximum number of clustered customers of each category among the seasons are reported, differentiated for the two available databases. Since the selection of valid profiles considerably reduced the initial number of customers included in the databases, and the final number of analyzed customers may vary from one season to another, in this study, in order to maximize the size of samples for each season, all the profiles that pass the pre-processing stage for the single season are used. From the data in Table 3 it can be seen that the outliers eliminated by the iterative procedure described above were very few in number. The percentage of reduction reached a maximum of 6.6% (i.e., in the residential customers with a main residence contract in the 2017 database).  
Unfortunately, the number of prosumers is very limited in the two databases--only a few hundred customers were prosumers. Thus, despite the importance of this category of LV customers, they are disregarded in the performed analysis.