# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 3 Proposed Approach
### Feature Selection and Data Pre-Processing  
The selection of the features that can impact end-user's electrical behavior is a sensitive matter. Often, the choice necessarily falls onto the limited information provided by the available database. Such initial selection can also impact the phase of association of new customers to a set of LPs. In the literature, various studies on the factors that influence the final electricity consumption, which should be considered key features for modeling the load demand by building representative LPs, have been proposed. Factors that strongly impact the final use of the electricity are type of contract (e.g., in many cases, distinguished only into residential and other usages) or the rated power. Still, many other exogenous features could also be important for differentiating the LPs. For residential customers (the most difficult to deal with), the most relevant socio-demographic factors are the household size [4; 31], the type and number of electric appliances in each home and their usage [31], the number of persons living in the household, how many of them are expected to be at home at the same time [32], salary [31], and employment status [4]. Finally, the weather is recognized as having a significant influence on electric consumption, and its role would be included in any analysis of customer electrical load patterns [32; 33].  
Unfortunately, most of the databases do not include exogenous information about the customers or their habits, home appliances, etc. Thus, the proposed approach's idea  
Figure 1: Flowchart of the proposed methodology.  
is to operate a first segmentation that filters the features that can mainly be considered responsible for the load shapes' diversity. The first segmentation aims at considering, one by one, groups of customers nominally homogeneous (e.g., the same type of supply contract or same geographic area). In particular, the main dichotomy is made between residential and other usage contracts. Moreover, among the residential customers, the difference between main and secondary residence contracts and, among the other usage contracts, depending on the economic sector (i.e., agricultural, industrial, or commercial customers), are considered. Finally, in this paper, as is the common practice of DSOs and without affecting the generality of the approach, the year into quarters is split, and three typical days for each quarter are considered (i.e., working days, Saturdays, Sundays, and holiday), as in [1,34]. It is worth noticing that each formed group of customers and, within the same group, each typical day is independently handled.  
Nevertheless, the features to be clustered within the same group of customers remains to be selected. Since the proposed approach aims to discriminate between the shapes of the customers' daily consumption profiles belonging to the same category, the selected features can be the time-series of the load consumption pattern. In this work, the only active power measured profiles are considered, but the same procedure can be applied to the reactive power patterns.  
Since the load consumption database contains raw data, a pre-processing phase is indispensable for removing missed samples and bad data, as well as reducing the number of features to be handled in the next steps. For instance, for filling the missing data in a given customer's time series, the samples' average relevant to the same time interval in the same typical day is used. This pre-processing activity is crucial but long and burdensome, and the reliability of the obtained outcome strongly influences the accuracy of the following stages. The result of this step is new subsets of load-measured profiles related to customers for which there are in the database enough data for considering them \"valid\" to be analyzed.