## 5 Conclusions  
Modern power system community has recognized the need to upgrade the role of the consumer in competitive energy market. The installation of smart metering is supported by the current legislative framework of European Union. The ideal case is that every consumer operates a smart meter. However, when the techno-economic barriers are present, alternative approaches should be considered to derive the typical demand patterns of the consumers. In many electricity networks, high-level consumer macro-categorization like residential, industrial and others is not robust. More detailed categorization is needed. The term \"load profiling\" refers to set of processes that lead to the characterization of the demand patterns of various consumers' categories. Load profiling is a flexible tool that can aid in the formulation of the typical patterns for single consumers or group of consumers. The load curves are grouped together based on their similarity. Usually, no other parameters are needed apart from the load data. The importance of efficient load profiling is evident in a wide range of contemporary research topics like demand side management, tariff design, load forecasting and others.  
Load profiling has gathered the attention of researchers in the recent years. This led to proposing many algorithms for clustering various load data sets. In the majority of the papers, the performance of the algorithms is tested only with quantitative criteria, namely the adequacy measures or clustering validity indicators. In spite of the large number of researches, no single study has provided a framework capable of indicating the benefits and limitations of the algorithms through a detailed comparison. In the present paper, a systematic procedure is proposed to rank the majority of the algorithms proposed in literature. The comparison includes 30 algorithms using 6 validation criteria. Apart from the validity indicators of the literature, the criteria involve factors that refer to the complexity of an algorithm and its availability. The main conclusions of the paper are summarized in the following:  
* Partitional algorithms are ranked 1 st if only validity indicators are used. In 10 indicators, a partitional algorithm ranks 1 st. The most robust partitional algorithm is Modified K-means#1. It ranks 1 st in 3 indicators and 2 nd in 5. The minCEntropy follows as it ranks 1 st in 3 indicators also. No fuzzy and neural-network based algorithms are present in the lists of Table 8. From the algorithms of the rest category, IRC, CLA, SVC and BCEC2 are present. The CLA is the most robust algorithm from this category.
* Computational time is an important factor. In this comparison, hierarchical clustering outclasses the other categories. SOM, AVQ, FDL and ISODATA are not recommended due to high time requirements.
* ISODATA and SOM are not recommended in problems where low complexity in terms of input parameter requirements is crucial. In this case, hierarchical algorithms are preferred.
* Software implementation availability is significant in cases of lack of programing skills, need for tested and verified codes or other factors. According to this criterion, hierarchical clustering, K-means, K-medoids and FCM are available in commercial and freely distributed packages.  
The present paper can serve as a guide for further algorithms comparisons and testing. Potential expansions of the developed framework may include further criteria and MCDA methods for evaluation. Also, the analysis will be applied in other data sets.  
[PERSON] performed the research and wrote the paper. [PERSON] revised the paper and set the objectives.  
**Conflicts of Interest:** The authors declare no conflict of interest.