# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 3 Proposed Approach
### New Customers/Cluster Association  
The procedure's goal is to associate an appropriate consumption profile to new customers that cannot be characterized by a historical behavior (lateral path in Figure 1). The clusters obtained with the previous classification are analyzed to identify the features common to all members of the clusters. In this way, each cluster is identified with a small number of features that can be used to associate the cluster with a new customer. These features must be stored in the DSO's customer database and be minimal to make their use as easy as possible. Annual energy consumption, geographic area of the customers, and peak hours are examples of features that can be used. Only features that are not used  
Figure 5: Flow chart of the second stage of clustering: inner optimization. in the preliminary dataset segmentation can be used for this task. For instance, if the customers have been preliminarily subdivided using the geographic area they belong to, this information is useful for identifying the corresponding set of representative clusters. Still, it cannot be used for the association of a new customer. Several features can be tested for characterizing the cluster. The first one is related to the climate. For instance, in Italy, the Italian national territory classification in climatic zones may be used. A \"climatic zone\" is identified by one of the first six capital letters (A-F) that attribute to each Italian municipality typical climatic characteristics [39]. A second feature is annual energy, particularly important for residential customers because it can be related to exogenous variables, such as the number of appliances and the annual salary. Other features are the seasonal or monthly peak values of the real profiles or of the centroid of clusters. Other tested features may be the geographic information, as region, province, and municipality, but if the available database does not uniformly cover the territory of interest (e.g., some regions are more represented than other ones), these features cannot be very useful for characterizing the clusters. Any other feature that the DSO could easily know should be evaluated to make this association the most reliable as possible. However, once a limited number of features characterizes each cluster, the values assumed by such features drive the association of a new customer to the representative LPs. In this paper, the LP attribution is performed with two different goals:  
* representing a single customer (new or existing but not monitored);
* representing a group of customers fed by the same secondary substation. For the first goal, the association is performed by assigning a score to each obtained cluster. The score is based on comparing the value of the feature for the specific customer and the value of the same feature calculated for each cluster. For instance, let us assume that the selected feature is the annual energy consumption. The customer's annual energy consumption is compared with the mean annual energy of the customers belonging to each cluster. The cluster that gains a score of 1 is the only one with the minimum distance. The other clusters gain 0 as a score for this feature. By repeating this counting for the variable number of the selected features, the cluster with the greatest score can finally be associated with the customer. For the second goal, a simplified method for associating an LP to one group of customers is developed. The combinations of the annual energy of the customers (calculated or estimated) and their geographic location are used for identifying groups of customers (e.g., the northern customers with a yearly consumption in the range 1500 kWh/year \(\div\) 2000 kWh/year). Thus, each cluster is characterized by the share of customers' groups used to guide the association of customers and LPs. For defining the share of the \(i\)-th group of customers in each \(k\)-th cluster, the distribution coefficients \(q_{ik}\) are calculated as in Equation (2).