# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 2 Load Profiling
The described methodology aimed to characterize the consumption patterns of existing and new secondary substations (i.e., LV/MV interfaces) in a probabilistic way. The article [22] focuses on reducing computational complexity of load profiling by preserving the information content in load patterns. To this aim, a method to analyze fine-grained smart meter data on the basis of singular value decomposition and wavelet energy entropy is proposed. Five features are identified as the compromise representation of load profiles for reducing computational cost while preserving accuracy. In this paper, the authors made use of principal component analysis (PCA) for reducing the dimensionality of the data to be clustered. In [23], load profiling was undertaken for gathering information useful for designing demand-side flexibility strategies. The \(k\)-means algorithm was used to segment households according to the electricity pattern while the household characteristics were studied to determine the influences on the electricity consumptions. In [24], load profile segmentation was developed for market settlement purposes. The hierarchical clustering approaches with Euclidean and dynamic time waring distances and average and Ward's linkage criteria were compared offline for analyzing measured consumption profiles of residential, commercial, and industrial consumers. Furthermore, the partition obtained by imposing several final numbers of clusters (5, 10, 15, and 20) were studied offline for identifying the most convenient set up. The authors of [25], starting from one year of smart meter electricity demand data from 656 households in Switzerland, compared two clustering approaches (\(k\)-means using daily load shape and \(k\)-means using daily profile features). The use of the daily profile features allowed to identify three distinct patterns over the course of the day, which challenges the assumption made by Swiss energy regulation that one standard pattern fits all homes [25]. In [26], three algorithms are compared: \(k\)-means, bisecting \(k\)-means clustering, and Gaussian mixture model-based clustering using a dataset of about 4000 buildings from different climatic zones and characterized by different purposes. Three fundamental load shapes were identified and to each building a dominant profile is assigned. In [27], daily profiles for typical buildings were identified using a two-level clustering on the basis of Gaussian mixture model and on hierarchical clustering. Two-year hourly electricity consumption of 40 university buildings with different purposes were processed. The comparison between the proposed approach and the surveyed literature may be made considering different aspects, e.g., (i) the size of the used database (i.e., duration of the measurement campaign, number and variety of customer category investigated), (ii) the association of one or more than one profile to customers belonging to the same category, (iii) the choice of the optimal clustering algorithm among different techniques (during the load profiling process or offline), and (iv) the ability of assigning a proper LP to a single or (v) to a group of customers. The dataset size used by the surveyed papers spans from the yearly profile of one industrial consumer only [14] to a few tens of two customer categories (residential and small enterprises users) [22], to hundreds of consumers without category characterization [15] or residential only [24; 25], to a few thousand (i.e., 2771 in [20], 5000 in [17]), to a few tens of thousands of residential customers only [16; 23]. Other studies refer to load profiles of aggregated customers, i.e., at level of primary substations (few hundreds in [19]), or at level of secondary substations (tens of thousands in [21]), or referred to buildings (tens in [27], thousands in [26]). An outstanding database is used in [18]--it includes over 6 million LV clients.