# Biclustering of Smart Building Electric Energy Consumption Data
## 3 Experimental Results  
In this section, we present a summary of the most representative results obtained on the data presented in Section 2.1. Firstly, we present the general behaviour of the five selected buildings in order to identify some of their interesting features. Table 3 shows the average electric energy consumption of the considered buildings for each year. This information can provide us an idea of the kind of energy consumption profile each building presents. From the presented table, we can conclude that Building 20 exhibits the highest energy consumption. This was expected, since this building hosts many biological laboratories, which require a high amount of energy to operate, and also for its size (one of the largest buildings in the experiment, see Table 2 for more details). It can also be noticed that Building 8 exhibits the lower energy consumption. It is worth mentioning the difference, in terms of energy consumption, between this building and Building 4 that is a similar building (see Table 2). On the other hand, Buildings 11, 45 and 4 present a similar general consumption rate, even if their characteristics are very different, being Buildings 45 and 11 much larger. With the aim to find some general consumption profiles, a preliminary study on these buildings was also conducted using a clustering technique. In particular, we applied the well-known k-means clustering algorithm with the aim of finding electric energy consumption profiles for the buildings of the campus. We selected three clusters for k-means clusters since there are three different types of buildings, so we expect that they will be grouped together. The conclusions regarding the considered buildings were that only Buildings 4 and 11 were grouped together, while the others were found to have a overall different profile. Again, it is surprising that Building 8 is not grouped together with Buildings 4 and 11, as the characteristics of the three buildings are similar (see Table 2). To obtain biclusters, we applied SMOB to the five selected buildings for considering the five years, and we extracted 45 biclusters. We also ran experiments considering each year separated, but the results are similar to the ones considering the whole year range, and, for this reason, as well as for reasons of space, they are not reported in this paper. The parameter of the algorithm used in the experiments are summarized in Table 4, which were tuned after performing various preliminary results. The average features of the 45 biclusters found on the data are presented in Table 5.  
We can notice that, on average, four buildings were grouped together for an average period of 51 days (see Table 5). This result is surprising, considering that at least two buildings (Buildings 20 and 8) show average consumptions that are significantly different from the other three buildings (see Table 3), and that also the characteristics and the use of the buildings are different. This results show that, even if the general energy consumption profile of the buildings is different, there are period of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Building** & **2011** & **2012** & **2013** & **2014** & **2015** & **Average (Kwh/m2)** \\ \hline
4 & 7.87 & 7.31 & 6.36 & 8.13 & 9.35 & 4.00 \(\times\) 10\({}^{-3}\) \\
8 & 1.29 & 1.50 & 1.47 & 1.33 & 1.35 & 7.12 \(\times\) 10\({}^{-4}\) \\
11 & 8.65 & 10.62 & 7.52 & 5.85 & 5.56 & 1.46 \(\times\) 10\({}^{-3}\) \\
20 & 26.98 & 24.65 & 23.39 & 22.11 & 29.87 & 3.66 \(\times\) 10\({}^{-3}\) \\
45 & 6.85 & 8.95 & 8.11 & 8.49 & 9.53 & 1.19 \(\times\) 10\({}^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Mean electric energy consumption information per building, in kWh. In the last column, the five-year average consumption is presented in terms of Kwh/m\({}^{2}\). time where buildings present the same behaviour.