## 5 Results
This translates into a difficulty for the model to generalise in these cases, as mentioned above, hence the greater degradation in the classification accuracy for these two appliances compared to the refrigerator, as can be seen from the comparison of the results in Tables 4 and 5. The Figures 2 and 3 show an example of the results of the algorithm for a single day of data, for the _seen_ and _unseen_ cases respectively. In both figures the lower graph shows a day of the dataset testing portion for houses 1 and 2 respectively. The dishwasher is characterized by a period of heating of the washing water followed by a period of washing and a phase of rinsing with warm water. On the other hand, the washing machine is characterised by a water loading and heating phase followed by a washing phase in which the highly variable absorption is due to the electric motor that drives the basket. In both cases the washing cycles and the maximum absorption of the appliances (and also for the refrigerator, even if in a less evident way) are clearly different. The upper graph for the two figures shows the aggregate load and the estimated average consumption for the appliances examined. As mentioned above, the network has been trained and tested twenty times for both the _seen_ and the _unseen_ cases, and in both tables are reported in addition to the mean value of the ensemble of the results obtained from the 20 models also the intervals of variation of the results obtained for the individual instances. The scores are more stable for the Table 4, highlighting the need for training conducted on a larger number of users, in order to further improve the performance of the algorithm. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Fridge** & \multicolumn{3}{c}{**Dishwasher**} & \multicolumn{2}{c}{**Washing Machine**} \\ \hline  & **Ensemble Mean** & **90\% Interval** & **Ensemble Mean** & **90\% Interval** & **Ensemble Mean** & **90\% Interval** \\ \hline Precision & 0.892 & (0.883, 0.898) & 0.788 & (0.738, 0.826) & 0.858 & (0.811, 0.893) \\ Recall & 0.851 & (0.841, 0.861) & 0.835 & (0.768, 0.897) & 0.869 & (0.827, 0.918) \\ Accuracy & 0.905 & (0.900, 0.908) & 0.989 & (0.987, 0.990) & 0.997 & (0.996, 0.998) \\ F1 score & 0.871 & (0.863, 0.876) & 0.809 & (0.790, 0.822) & 0.863 & (0.835, 0.900) \\ MCC & 0.796 & (0.786, 0.803) & 0.805 & (0.784, 0.817) & 0.862 & (0.834, 0.899) \\ MAE [W] & 17.03 & (16.82, 17.24) & 33.07 & (31.19, 35.68) & 8.31 & (7.88, 8.70) \\ SAE & \(-\)0.046 & (\(-\)0.066, \(-\)0.025) & 0.063 & (\(-\)0.054, 0.219) & 0.014 & (\(-\)0.059, 0.115) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the method for the _unseen_ case for house 2 of the UK-DALE dataset. In total, 20 different instances of the neural network have been trained and the value of the ensemble estimate and the 90% range of performance variation are reported. Figure 3: Example of load disaggregation for the _unseen_ case. The upper graph shows the aggregate load and the estimated consumption obtained by the method, while the lower graph shows true values of the power absorbed by the three appliances. The estimated value of the consumption of household appliances in the upper part of the figure is assumed to be constant for the activation periods and equal to the average consumption during use. Figure 2: Example of load disaggregation for the _seen_ case. The upper graph shows the aggregate load and the estimated consumption obtained by the method, while the lower graph shows true values of the power absorbed by the three appliances. The estimated value of the consumption of household appliances in the upper part of the figure is assumed to be constant for the activation periods and equal to the average consumption during use.