## 1 Introduction
### Literature Survey and Contributions
The utility has pre-defined consumer classes and the authors apply two clustering methods, the combination of a SOM with the hierarchical algorithm run with the complete linkage criterion and a SOM with the K-means, in order to compare the existing load profiles and the estimated by the clustering process. The SOM/K-means combination leads to more robust clustering. The Hopfield recurrent neural network for clustering load curves is introduced in Reference [55]. The set is composed by medium voltage consumers and the performance is checked by 3 validity indicators. The combination of SOM and K-means is also used in References [56; 57]. In Reference [56], the scope is to derive representative base load profiles for a set of buildings in Korea for application in demand response measures. In [57], the data set refers to the total consumption of an industrial park in Spain. The data cover a period of 3 years. The authors employ different SOM per month. In Reference [58], the data set consists of 471 non-residential consumers. The FDL is applied to create several clusters. Next, the authors provide a discussion on tariff design per cluster. The same algorithm is used in References [59; 60]. The authors propose a data representation method using harmonic components in the frequency-domain and compare variants of the frequency-domain representation with the conventional time-domain one. In Reference [61] the Competitive Leaky Algorithm (CLA) is introduced in the load profiling literature. The algorithm bases its operation on competitive learning. The authors apply it to the daily load curves of active and reactive load of a high-voltage consumer. The ISODATA algorithm is employed in Reference [62] for clustering the load curves of 660 hourly metered consumers. The results are compared with the existing load profiles classes of a Finnish utility. The 2 nd type of papers includes a comparison among algorithms in order to define the most suitable algorithm for a specific data set. Early analyses are found in References [63; 64; 65]. In Reference [63], the comparison includes two versions of hierarchical agglomerative clustering, namely, Ward and average linkage, FCM, K-means FDL and SOM. The comparison is held via 4 adequacy measures, namely the Mean Index Adequacy (MIA), Similarity Matrix Indicator (SMI), Clustering Dispersion Indicator (CDI) and Davies-Bouldin Index (DBI). The algorithms are executed for 10 to 20 clusters. According to MIA and CDI, the FDL wins the competition but SMI and DBI indicate that the superior algorithm is the K-means. In [64], the hierarchical clustering and FCM are compared only in terms of the shapes of the load profiles that lead to. It should be noted, that an algorithm is more efficient than the others only for specific number of clusters. This is evident in [65]. Utilizing the CDI, the FDL leads to lower errors for large number of clusters (i.e., above 22) while average linkage hierarchical clustering is more efficient for small number. The analysis of [63] is enriched in [66] by adding 2 measures, namely the Scatter Index (SI) and the Variance Ratio Criterion (VRC). Again, the extraction of the optimal algorithm is a matter of the selection of the validity indicator. In order to address the problem of the strong dependence of the K-means in the selection of initial centroids, 2 modified versions of the algorithm have been proposed in [27; 28; 67]. The modified versions seek to extract the optimal combination of 2 calibrated parameters that define the initial cluster centroids. With this approach, the initial centroids of the K-means are chosen based on the best results of each one of 6 adequacy measures. The proposed versions of the K-means present better performance when compared with the FCM, the family of hierarchical algorithms, 1D and 2D SOM and the adaptive learning quantization algorithm. References [68; 69; 70; 71] propose two initialization methods in order to enhance the performance of the K-means. The first approach is the Weighted Fuzzy Average (WFA) K-means.