# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 1 Introduction  
The electrical load knowledge has always been essential for most applications and studies on the power system regarding network operation and planning. It is well established that it is not sufficient to estimate the total load demand or the total production in a given instant or forecast them in a specific time horizon. The time series of both consumption and generation is imperative for adequately representing the impact of demand coincidence and of the, often low, generation-load homotheticity, which are the causes of voltage regulation issues and power congestions, especially at the medium-voltage (MV) and low voltage (LV) levels where most distributed energy resources (DERs) are connected. This is even more true nowadays since new loads, characterized by coincident peaks of consumption (e.g., home charging of electric vehicles) or by high absorption peaks (e.g., heat pumps or cooking appliances) are increasingly frequent. Moreover, the massive diffusion of non-programmable renewable energy sources (RES), dispersedly connected to the distribution system, causes severe operation problems, and gives a leading role to the flexibility of demand, which is crucial for the success of the energy transition. Both conventional and emerging stakeholders of the power system, transmission and distribution systems operators (TSOs and DSOs), regulators, market players such as balance responsible parties, and aggregators are interested to accurately model the behavior of customers with load profiles (LPs). LPs are patterns of electricity load consumption of a customer or a group of customers over a given period that have been extensively used for many years. Generally, LPs areobtained from historical data or measurement campaigns, suitably elaborated for defining representative or typical consumption shapes. Unfortunately, the LP updating is not sufficiently frequent, and, often, LPs refer to customers that no longer exist. Moreover, the current LPs are often based on measurement campaigns that involve a small number of customers not significant as a statistical sample of a defined ambit (e.g., a region or a country). Such LPs can capture only a portion of the end-users and cannot represent modern consumers, particularly the new prosumers. Indeed, even the most accurate LPs that associate with homogeneous groups of end-users one profile per season and the day (workday, weekend, pre-holiday, holiday), generally obtained by averaging the relevant samples, are not so realistic. These models consider daily curves applicable to groups of consumers subdivided on the basis of the contract or economic activity (i.e., residential, agricultural, industrial, or tertiary) [1]. Nevertheless, assuming a certain homogeneity among end-users and neglecting the actual variability among consumers' consumption habits belonging to the same category is acceptable for almost all economic activities but is not valid for residential consumers. The residential users exhibit a tremendous variance of load profiles that depends on several exogenous factors (i.e., household size, the number of persons living in a household, net income and employment status, level of education, etc.) [2; 3; 4]. Hiding single customers behind a large and non-homogeneous group's average behavior leads to significant errors in distribution studies. One typical example is represented by the time coincidence of the demand in residential neighbors that is considerably smaller than the one that can be achieved by superimposing the same load profile several times. Consequently, models that associate to all the customers belonging to the same category one day profile only, even differentiated by season or typical day, result as inaccurate, mainly if the category is residential. Nowadays, new and more accurate measurements can be gathered from the advanced metering infrastructure (AMI) that uses the second generation of smart meters.