## 1 Introduction
### Literature Survey and Contributions
Each indicator measures either the compactness, the separation or both of the formulated clusters. 3. Apart from validity indicators, no study provides further criteria to strengthen the conclusions on algorithm selection. The contributions of the present paper to the load profiling literature are described in the following:  
1. In the present study, a comparison of the most common algorithms of the literature takes place. More specifically, 30 clustering algorithms are compared using 12 validity indicators. To the best of the authors' knowledge, this is the first study that considers this number of algorithms and validity indicators. The scope is to gather the majority of the algorithms under a common analysis in order to discuss their advantages and disadvantages and provide the interested parties a guide on algorithm validation and selection. 2. All the studies of the literature that include a comparison use only strictly mathematical criteria. In this study, additional 5 criteria are introduced. This is justified by the increase of smart meter installations across the globe. This fact will lead to the collection of vast amount of Big Data;an efficient algorithm should not only lead to robust clusterings, as measured by the validity indicators but should correspond to low complexity in terms of input parameters requirements and execution speed. 3. The TOPSIS method is implemented in order to reach safe conclusions regarding the selection of an algorithm that satisfies a number of contradicting criteria. It should be noted that apart from extracting information about demand patterns, load profiling is an important tool that has been employed in various applications such as load forecasting, retailer profit maximization, scenarios generation for optimization problems, demand side management implementation, load dispatching and others [78; 83; 84; 85; 86; 87; 88]. The combination of clustering and forecasting system is a promising approach [84]. This paper considers a feedforward back propagation neural network. While back propagation models have been widely used in forecasting problems, the forecasting results can be different when the number of epochs of back propagation training is changed, a fact that is discussed in [89]. To address this problem a novel time series forecasting approach is introduced in [89] where a series of deep belief networks generate different forecasts and they are combined through the application of support vector regression model. Thus, the potential of implementing the clustering tool within the methodology presented in the aforementioned study is high. Another promising approach in load forecasting is introduced in [90]. A least square classifier is utilized with a random forest method. The proposed method outperforms other models such as random forest, feedforward neural network, support vector regression considering load forecasting tasks for five states in Australia. Due to the diversity load profiling potential applications, an imperative need to define the optimal algorithm rises. In the following sections a short description of the algorithms is provided together with the validation framework. Also, a detailed discussion of the results is included.