## 5 Results  
The first case examined is the one called _seen_. The Table 4 shows the results obtained for the household appliances examined both in terms of identifying the state and in terms of estimating instantaneous and overall consumption over time. The results are very satisfactory for all three appliances considered, as far as the identification of the activation status is concerned, in particular for the most energy consuming appliances and less for the refrigerator; the latter has a less evident _signature_ as its maximum absorption is much lower than that of the dishwasher and washing machine. The energy performance in the strict sense is also very good, especially for the SAE metric, which identifies the overall energy consumption, with errors of around 3% of the actual value for the refrigerator and below 10% for the dishwasher and washing machine. The MAE measures the error in the instantaneous power estimation, the fact that by methodological choice the power has been modelled as constant during the whole activation period affects the maximum performance achievable on this metric. The Table 5 shows the results obtained on the dataset for the _unseen_ case. The performance achieved in estimating the operational status is worse than in the previous case as expected. On the contrary, the energy performances are in line with the numerical results obtained for the _seen_ case. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fridge**} & \multicolumn{2}{c}{**Dishwasher**} & \multicolumn{2}{c}{**Washing Machine**} \\ \hline  & **Ensemble Mean** & **90\% Interval** & **Ensemble Mean** & **90\% Interval** & **Ensemble Mean** & **90\% Interval** \\ \hline Precision & 0.875 & (0.867, 0.885) & 0.942 & (0.904, 0.966) & 0.975 & (0.968, 0.979) \\ Recall & 0.859 & (0.846, 0.871) & 0.919 & (0.890, 0.942) & 0.982 & (0.977, 0.987) \\ Accuracy & 0.880 & (0.878, 0.882) & 0.997 & (0.995, 0.997) & 0.997 & (0.996, 0.997) \\ F1 score & 0.867 & (0.864, 0.870) & 0.930 & (0.905, 0.946) & 0.978 & (0.976, 0.980) \\ MCC & 0.759 & (0.755, 0.762) & 0.928 & (0.903, 0.945) & 0.977 & (0.974, 0.979) \\ MAE [W] & 15.25 & (15.08, 15.47) & 20.41 & (19.99, 21.00) & 41.97 & (41.80, 42.21) \\ SAE & \(-0.020\) & (\(-0.046\), 0.002) & \(-0.042\) & (\(-0.082\), \(-0.005\)) & \(-0.077\) & (\(-0.085\), \(-0.066\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the method for the _seen_ case for house 1 of the UK-DALE dataset. 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. The data for the dwelling not seen during the training of the network, differ from those for other houses. In house 2, in fact, there are unmonitored appliances that are different from those in other households, thus changing the characteristics of the \(e(t)\) disturbance signal present in the overall load, which is added to the consumption of the three appliances under examination. Moreover, the models of fridge, washing machine and dishwasher are different from those present in the other two houses used in the training: in terms of the power used, the length of the operating cycles and the evolution of consumption itself. In this case, the network has the difficult task of generalising the absorption of a category of household appliance and recognising its footprint in the overall consumption data. Despite this, we can say that the performance is surprisingly good considering the limited dataset used for training, which although having a significant temporal extension, is related to a few users and therefore does not have many examples of appliances of the same class that would allow a better generalization in the training phase. The washing machine and the dishwasher may be subject to greater differences in consumption profile, also in relation to the different washing programs available.