## 4 Discussion  
This study and its results have tried to make several contributions. First, was to find information on building occupancy, which is usually not available, by using the data which is more common in buildings. Secondly, to increase the value of energy and water consumption sub-metering in commercial buildings. Last, is to promote the smart building concept where one data stream can be used for multiple purposes. Further research is needed to reinforce these contributions, especially regarding scaling the methods to real-world applications. Foremost is to mention the limitations of this study, which are concerning the data collection of ground truth and water consumption. Ground truth was collected with people counting software, which reported that more people arrive at the office than leave it. This limitation is concerning the supervised method while giving another motive for the exploration of unsupervised methods. Despite this limitation, other possible methods for continuous long-term occupancy were not possible in our case study and are probably of even lower accuracy. Furthermore, the goal of this study was not the development of the method of high accuracy, but a method that will give more insight than usually available. Regarding water consumption, the issue is that it is not sub-metered for the floor, which might decrease its correlation with measured occupancy. This limitation could be one of the reasons why water consumption showed lower results with the unsupervised method, while with the  
Figure 18: Occupancy day profiles coloured by clusters of water consumption. supervised method, it was combined with other predictors. Additionally, to note is that sub-metering of water consumption is very rare in Finland. Two methods of data mining studied in this work have been successful in discovering information about the office occupancy from the sub-metering. With the supervised method, this study proved that it is possible to predict the number of occupants in a larger office facility by using metered equipment, lighting electricity consumption or using water consumption. Earlier studies have mostly worked on detecting the occupancy or the occupancy level on a smaller scale. On the other hand, to use the supervised method, a long-term continuous collection campaign of ground truth is necessary. One way to do it is by installing the cameras with people counting software above all entrances to the target building area for a long enough period (30 to 90 days by the results of this work). In our experience, the biggest challenge was the installation permission of cameras, since that brings privacy and security issues. Permission was needed from the building owner, tenant and the IT department. Scaling this solution to a broader group of buildings could be problematic if the privacy issue is not possible to solve. Therefore, other methods, e.g., unsupervised methods, are appreciated where ground truth data is not required. The unsupervised method developed in this work was able to cluster the days by the level of the office occupancy using equipment electricity consumption. In the case study office, the three levels of occupancy were found: zero (low), medium and high. Regular working days belonged mostly to the high occupancy level and were not divided into more than one level. A reason for this could be that even though individuals can work remotely, the overall office occupancy pattern does not have clear dividable groups. The medium level contains mostly days that have occupancy between a normal working day and a public holiday. These days are usually between holidays and weekends or during vacation periods. Although it is important to note that medium and high occupancy levels do not have a clear boundary, which means that some of the days could belong to either of the clusters. The way of working could change radically in the future and remote work will become more common. This means that there could be fewer regular working days and more irregular days.