## 1 Introduction
[21], clustering was used on energy consumption from 10 institutional buildings to improve the building energy consumption forecasting. Following the studies on the larger scale, clustering has been emerging on the building level, where the energy consumption from one or several buildings has been clustered for finding either occupancy energy usage patterns or the actual occupancy patterns. In a study by [PERSON] and [PERSON] [22], the \(k\)-means clustering algorithm has helped the authors to determine the number of typical occupancy patterns in a presence dataset from 16 single office rooms. While centroids of those clusters were used to create occupancy profiles, which could be used as an input to the building performance simulation. A similar study, but on a larger scale of the whole building, was done by [PERSON] et al. [23] where \(k\)-means clustering was used to find the occupancy patterns, while profiles were created using the Decision Tree supervised method. Authors have managed to create the occupancy profiles and predict their occurrence using time of day, weekday and season. The highest uncertainty in the occupancy profiles prediction has shown to be at the start and end hours of the working day. Another work, where the aim was creating inputs for building simulation was done by [PERSON] et al. [24]. They have proposed a data-driven approach for modelling of occupancy and occupancy-related electric load profiles. They have used a two-step clustering approach with \(k\)-means and Self-Organising Map (\(SOM\)) to find patterns in electricity data from households, which k-NN algorithm used to create a yearlong profile. The occupancy presence profile was created from electricity consumption with the method from the previously mentioned work of [PERSON] et al. [12; 15]. So far, the mentioned research has mostly tried to find patterns in a particular variable by clustering that same variable. The attempt to find the pattern of an unknown variable by clustering known and correlated variables could bring additional value to existing data in the building. This could be beneficial for finding occupancy patterns of buildings since occupancy is rarely measured in the buildings because it is difficult to measure. [PERSON] et al. [13] have done this by applying the clustering on the variables which significantly correlate with the occupancy, such as CO\({}_{2}\) concentration, electric power, room air temperature, humidity, air conditioning operation mode and window opening status. The authors have used these variables with hierarchical clustering to identify the occupancy patterns of a single person office. Clusters of measured variables by the time of day were then compared with the ground truth clusters. The analysis has shown that using electricity consumption was a good predictor for the occupancy state during morning and afternoon while CO\({}_{2}\) was also a good predictor during lunchtime. They have shown that it is possible to use commonly measured variables such as CO\({}_{2}\) concentration and electricity consumption to predict the occupancy patterns of a single person room. On the other hand, the proposed method requires much effort to be used for identifying the occupancy patterns of just a single room, which makes the scalability of the method difficult in practice. The analysis of the literature highlights the potential that building operation data has with the data mining methods. Studies that used supervised data mining methods with smart metering data have proven that it is possible to detect occupancy presence or its range, mostly in residential and in some small-scale office environments. Other studies that used unsupervised methods managed to group the consumers by their consumption patterns, or they successfully created the occupancy or occupancy-related electric load profiles from the historical measurements of the same values. [PERSON] et al.