## 1 Introduction
### Literature Survey and Contributions  
Two general models of load profiling are considered by utilities, namely the area based model and the category based model [20]. The area based model is adopted when there is not sufficient number of smart meter installations. Within a territory, at every time interval the consumption of consumers with smart meters (i.e., usually industrial consumers) is subtracted by the total (i.e., distribution transformer readings) and the remaining curve is deemed to the rest consumers with conventional meters. The area based model is appropriate in cases with low availability of data and high meter installation cost. The requirements of \(\Pi\) infrastructure (i.e., hardware, communication protocols, etc.) are lower compared to the category based model. However, it shortfalls in terms of accuracy, since it provides a simplified approach to load profile extraction. The category based model requires a considerable number of smart meters and a long period of systematic measurements. After the data collection, a data mining process takes place to formulate the load profiles. The process may refer to statistical analysis or to the implementation of clustering algorithms. For instance, static profiles are derived from existing historic data. The data classes are a priori known. If the data sample is sufficient then an average profile is extracted that represents the pre-defined class. Usually, the criterion for the classes' formation is the type of electricity tariff. Static profiles formation does not require continuous measurements and averaging. Dynamic profiles refer to periodically updating the static profiles and adjusting them by taking into account temperature variations and other factors that affect the demand in daily or seasonal basis [21; 22]. Load data profile generation is accomplished by load survey studies and clustering algorithms. Apart from consumption data, load surveys seek to gather weather data, consumer preferences, occupancy behaviour and others [23; 24]. The accuracy of load surveys depends on the characteristics of the eligible sample of consumers [25; 26]. Following a bottom-up-approach, the findings of load surveys on the eligible set are scaled up to include the rest consumers. This fact makes the clustering approach more flexible; different algorithms can be tested and no information on number of clusters is necessary. Also, the clustering approach requires only load data. While other variables such as temperature, tariff type and others may be incorporated in clusters, they are not mandatory. Clustering-based load profiling is a multi-stage process [27; 28]. The first stage refers to data cleansing, i.e., erroneous values detection and removal, missing data filling and others. Next, the 1 st stage clustering takes place. For each consumer separately, the set with the daily load curves is clustered. The average daily load of each cluster is actually the normalized load profile. A specific load profile is chosen for each consumer and a second clustering occurs on the selected load profiles to produce the consumer classes. Therefore, the 1 st stage clustering is held using the available daily load curves of each consumer and the 2 nd stage clustering uses the load profiles derived from the previous stage. The final consumer clusters and the consumer clusters load profiles is the product of the 2 nd stage. Note that the 1 st stage clustering can be avoided [29]. In this case, the load profile that will represent the consumer refers to the average daily load curve of the consumer's daily load curve set. These two stages can utilize one or more clustering algorithms.