## 3 Results
### Results of the Unsupervised Method
#### 3.3.2 Clustering Results  
In Figure 13, three clusters of equipment electricity consumption are presented, together with day profiles of consumption belonging to each cluster (black lines) and with the cluster centre (red line). Each of these clusters represents a typical equipment electricity consumption pattern for the case floor. Since the office equipment consumption was mostly influenced by occupancy (e.g., computers), the assumption was that by clustering this consumption, the occupancy patterns could be found. In Figure 14, the occupancy measured by people counting cameras is presented with each day profile coloured by the belonging cluster. The highest occupancy belonged to the first cluster (black) and the days with the lowest occupancy to the second cluster (red), while the days with occupancy in between belonged to the third cluster (blue). This figure shows that there was a good correlation between measured equipment consumption and office occupancy. Correlation was most visible in the second cluster, where occupancy was nearly zero and consumption was low, compared to the other days. The first and third clusters were not clearly divided since they contained some border cases, which meant that certain days could potentially belong to either of those clusters. This was not the case with the second cluster, where belonging days with their consumption (and occupancy) had a clear membership.  
Figure 14: Occupancy day profiles coloured by clusters of equipment electricity consumption.  
Figure 13: Equipment electricity consumption clusters. (**Black**) lines represent the day profiles of consumption, while (**red**) lines represent the cluster centre.  
Another way to analyse was when people had their summer vacation. Furthermore, in the same cluster, there were \"bridge\" days or days between the public holiday and a weekend, which some people took as a day off. These, a total of 29 days, were for the sake of simplicity called \"semi-holidays\" in this study. The largest group of the results was by looking at the date of the days in each cluster. In Cluster 2, all belonging days were public holidays in Finland, such as Labour Day (1 May), Easter Friday and Monday, Ascension Day and Midsummer Day. The days in Cluster 3, with its medium level of consumption and occupancy were days when many days were regular working days which were clustered to Cluster 1, with a total of 107 days. Based on this analysis, it could be said that Cluster 1 could be called a high occupancy level, Cluster 2 as a low occupancy level and Cluster 3 as a medium occupancy level.  
Following the equipment electricity consumption, the same method was performed on electricity consumption for office lighting and water consumption. Based on the findings from the Elbow method from Section 3.3.1, lighting consumption was clustered into four clusters, while water into six. Four clusters of lighting consumption are presented in Figure 15. In Cluster 3, days with zero and medium consumption were clustered together. In Figure 16, occupancy day profiles are coloured into four colours to represent the four clusters of lighting consumption. The lines representing lighting consumption day profiles were interlaced between different clusters, which meant that clustering the lighting consumption did not group the occupancy days into a logical order. The same conclusion can be seen from six clusters of water consumption (Figures 17 and 18).  
Figure 15: Lighting electricity consumption clusters. (**Black**) lines represent the day profiles of consumption, while (**red**) lines represent the cluster (**centre**).  
Figure 16: Occupancy day profiles coloured by clusters of lighting electricity consumption.  
Figure 17: Water consumption clusters. (**Black**) lines represent the day profiles of consumption, while (**red**) lines represent the cluster (**centre**).