## 1 Introduction
Information being discovered in those studies was ranging from grouping buildings or consumers by their energy usage, finding occupancy and consumption patterns or detecting and predicting the occupancy in buildings. In a study by [PERSON] et al. [12], supervised data mining methods were used to determine the occupancy presence on the smart meter data from five Swiss households during a period of eight months. The ground truth of the occupancy was acquired using the application on the tablet computer. Supervised methods used in this study were classification algorithms such as Support Vector Machines (SVM), K-nearest neighbour (KNN), thresholding (THR) and the Hidden Markov Model (HMM). Results from this work have shown that it is possible to achieve occupancy detection accuracies of over 80% from smart meters in households. Improving on this work, [PERSON] et al. [15] have managed to achieve average occupancy detection of up to 94% by introducing dimensionality reduction and extending the feature set and the set of classifiers. [PERSON], [PERSON] et al. [14] have worked on inferring occupancy in a university laboratory using smart meter data. Their work has tried to infer binary occupancy status (present, not present) and ranged occupancy (range of the number of present occupants). Since in the lab, the maximum occupancy is twelve persons, the authors have divided occupancy into four bins (0, 1-2, 3-5, 6-12). Authors have used the following classifiers: NaiveBayes, Random Forest, Decision Tree, Multilayer Perceptron and KNN. Results from this work have shown that it is possible to infer binary occupancy status and range with high accuracy and that the best classifiers have shown to be Multilayer Perceptron and Random Forest. Interestingly, the authors have experimented with the training set size and found that the best size was 8 days, while shorter or longer training set sizes increased the error. Supervised data mining was also used for detecting the occupancy on a workstation level in the office. [PERSON] et al. [16] used different features from data collected by smart electricity meters, which were installed on four employees' workstations. Classifiers analysed in the study were KNN and SVM. Overall, results from the study have shown that the KNN classifier performed the best, with the occupancy status detection accuracy of up to 94%. Studies that used supervised methods for occupancy detection from smart meter data were not only done on small scale. In a study by [PERSON] et al. [17], occupancy detection in residential buildings was performed on the dataset from 5000 buildings. Classification methods were Random Forest, SVM, KNN, neural network (NN) and gradient boosting. This work has shown that it is possible to detect occupancy in households with high accuracy even by using a big dataset. Furthermore, the authors have shown that classifiers could generalise very well across different households, which makes this technology highly scalable. Reviewed studies that used supervised data mining on smart meter data have all commonly been done with a goal to detect occupancy or at the best to determine the range of occupancy and most of them were targeting households. Furthermore, the dataset with high sampling frequency was used where electricity consumption was measured by every second. In recent years, unsupervised data mining methods, such as clustering, have been applied to building operational data in various studies. These methods have shown some encouraging results even though their applications are still at an early stage [11]. Earlier studies in the field primarily using clustering were trying to find patterns in the energy consumption on a larger scale (district or even a country level) and/or to group the consumers. Such studies have used electricity usage data from a large number of Chinese, Irish or American households to create residential electricity profiles to which consumers have been grouped and classified by their social status [18; 19; 20]. In a study by [PERSON] et al.