# Biclustering of Smart Building Electric Energy Consumption Data
## 3 Experimental Results
Such results suggests that the managers of the buildings should check the energy consumption pattern of the buildings more closely, to notice out of the normal behaviours that could yield a unnecessary use of energy or management issues. We can test if this hypothesis is valid by checking more closely some of the biclusters found. In this sense, we have selected four representative biclusters to show how this method is able to identify abnormal behaviours in a period of time. For example, considering Bicluster 19 depicted in Figure 3, we can notice that on 7-8 July, which was a weekend, the consumption of Building 20 decreased to the levels of Building 11 (17.13 kWh). This is odd, since in Buildings 20 there are many biological laboratories, which need constant energy to, for example, maintain the temperature. In contrast, in Building 11 there are only offices and classrooms, which on a Saturday should not consume much energy. Moreover, on the day after, a Sunday, the consumption of Building 11 was higher than that of Building 20. This fact represent an abnormal consumption for Building 20 since the average consumption during weekends in July through the years is 25.61 kWh, which is significantly higher than the consumption depicted in the bicluster. Moreover, on 14 July, a Sunday, we can see that the consumption of Building 11 was lower than that of building 20, which is the expected behaviour and can be found again on 21 July, which was a Saturday. Bicluster 31 (Figure 4) also presents some facts that are worth noticing. In this bicluster, only two buildings, namely Buildings 4 and 45, are included. These are very different buildings (see Table 2), being Building 4 a smaller building, while Building 45 is much bigger and has many computer laboratories. From our preliminary clustering study, they were considered to have an overall different energy consumption profile. This result shows how a biclustering method is able to find similar consumption patterns over certain periods of time, even when the overall consumption characteristics of each building are different. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Generations & 100 \\ Population size & 200 \\ Tournament size & 4 \\ Crossover probability & 0.85 \\ Mutation probability & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters used in the experiment. \begin{table}
\begin{tabular}{l c} \hline \hline
**Measure** & **Average** \\ \hline \(VE^{t}\) & 0.17 (0.11) \\ Var & 7.37 \(\times\) 10\({}^{5}\) (1.14 \(\times\) 10\({}^{6}\)) \\ Volume & 210.38 (56.43) \\ Buildings & 4.07 (0.69) \\ Days & 51.38 (11.13) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average information for the 45 biclusters found on the data. Standard deviation between brackets. Moreover, it can be observed that the behaviours of the two buildings captured in this bicluster are similar; the energy consumption of the two buildings drops during weekends (for example on 7, 13 and 20 September). However, it is strange that exactly during these periods, the consumption of Building 45 is lower than that of Building 4. This may reflect a bad management of Building 4, or may be due to the fact that Building 45 is newer and better equipped as far as energy efficiency is concerned. The same kind of behaviour can be found in Bicluster 35 (see Figure 5), where, in addition, we can noticed that Building 4 presents peaks of consumption that are higher than those of Buildings 45 and 11, which are bigger buildings. This is remarkable, as it represents an anomalous energy consumption given the size of the experimental buildings. Figure 4: Representation of Bicluster 31. In this biclustering, is worth mentioning that two different buildings present a similar consumption pattern during a period of time. Figure 3: Representation of Bicluster 19. In this figure, the abnormal consumption of Building 20 on 7–8 July period is depicted.