# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 2 Load Profiling
The authors of [14] proposed a general framework for supporting the analyst in selecting the most common algorithms used for load profiling through a multi-attribute decision-making technique for considering several conflicting criteria. The analysis's outcome highlights that the partitional algorithms (such as \(k\)-means) have better performance in classification validity. In contrast, hierarchical methods show a lower computational time and complexity. To provide a brief overview about the state of art of load profiling research activities and for highlighting the novelties and the strengths of the proposed approach, several recent papers regarding this topic were surveyed. Modified versions of the \(k\)-means algorithm dedicated to the load profiling analysis are proposed in [15]. The article describes a multi-stage load profiling framework in which the \(k\)-means algorithm plays a crucial role. The two versions of the modified \(k\)-means algorithm had an updated initialization stage, the initial centroids were selected linearly and not randomly, and extended quantiles were considered. The effectiveness of the proposed algorithm was verified on a limited set (i.e., 150) of customers of various types. A scalable load profiling algorithm was proposed in [16] to undertake big data processing. A growing self-organizing map was implemented on a parallel and scalable computing platform to achieve high data processing volumes in real time. The dataset analyzed consisted of about 10,000 Australian residential customers' readings measured with 30 min of sampling time. A method for the stochastic modeling of the smart meter data is proposed in [17]. The difficulties related to managing the high volume of data provided by the large-scale deployment of smart meter data were relieved by linearizing the energy consumption patterns. The linearized consumption patterns were then analyzed using a proposed clustering algorithm based on \(k\)-means, enhanced for improving the analysis of profiles characterized by a high intra-cluster similarity. In [18], a load profiling-based methodology to support the DSO investment decisions is proposed. The method uses hierarchical clustering for identifying similar load profiles at the distribution substation level for representing all days of a given period. The entire year was characterized by 12 typical load profiles obtained as the resulting clusters' centroids. A Markov process was used for representing the stochastic grid behavior starting from the typical profiles' behavior. The use of density-based spatial clustering of applications with noise (DBSCAN) as clustering algorithms is proposed in [19], together with the hierarchical approach, in order to capture customers' electric behavior and build typical load profiles, as well as in [20], for identifying suitable electricity retail price for residential customers. In [19], DBSCAN was used for creating a representative profile for each primary substation (i.e., high voltage HV/medium voltage MV interface), starting from the data obtained from daily measurement. The hierarchical approach was exploited for identifying the similarities among the different representative shapes of the primary substations. The optimal number of typical load profiles suggested for describing the Portuguese territory's primary substations was found by combining the clustering analysis outcome with geographic information and business intelligence. In addition, a statistical analysis of end-users' historical consumption regarding around 3000 profiles was conducted to better capture their consumption regularity and associate a single profile [20]. In [21], a hierarchical clustering approach was used for defining representative daily profiles from the measurement collected from tens of thousands of secondary substations distributed over the Portuguese territory. The representative daily profiles were then used for building a probabilistic distribution function that modeled the power consumption of each secondary substation in each instant of the day.