## 6 Discussion  
The use of a public dataset allows to evaluate the results obtained in comparison with other methodologies published in the literature. Unfortunately, not all of the published studies address both proposed cases, the _seen_ case, in which training and verification are conducted on the same home, and the _unseen_ case, in which verification is conducted on a different user than the one on which the network has been trained. The Table 6 shows the comparison of the performance of our model (TP-NILM) with the models in the literature for the _seen_ case. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Fridge**} \\ \hline  & **Prec.** & **Rec.** & **Acc.** & **F1** & **MCC** & **MAE** & **SAE** \\ \hline Neural-NILM CO [30] & 0.50 & 0.54 & 0.61 & 0.52 & - & 50 & 0.26 \\ Neural-NILM FHMM [30] & 0.39 & 0.63 & 0.46 & 0.47 & - & 69 & 0.50 \\ Neural-NILM AE [30] & 0.83 & 0.79 & 0.85 & 0.81 & - & 25 & \(-\)0.35 \\ Neural-NILM Rect., dAE, Deep AE, D-ResNet & Recurrent Neural Networks (Neural-NILM LSTM) and Extreme Learning Machine (H-ELM). & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison for the _seen_ case on the UK-DALE dataset. The models used for comparison are based on Combinatorial Optimization (Neural-NILM CO), Hidden Markov Model (Neural-NILM FHMM, ON-line-NILM), Convolutional Neural Network (Neural-NILM AE, Neural-NILM Rect., dAE, Deep AE, D-ResNet), Recurrent Neural Networks (Neural-NILM LSTM) and Extreme Learning Machine (H-ELM). The TP-NILM model offers good performances as far as the recognition of the state of activation is concerned, also the accuracy in the estimate of the total consumptions (SAE) is satisfactory, while the value of the MAE is affected by the assumption made about the constancy of the consumption for each cycle of activation. The best performance is obtained for high-consumption appliances, while the algorithm we have proposed seems to have more difficulty in correctly identifying the activations of the fridge. In this regard, it is worth remembering that we intentionally under-sampled the time series. In the case of the refrigerator, the maximum power absorbed is much lower than that of a dishwasher or washing machine so that the periodic activations can easily be confused with the noise associated with the many other unmonitored loads that contribute to the overall aggregate load. The absorption of the refrigerator, however, is characterized by the activation of a marked absorption linked to the starting point of the compressor, lasting less than a minute, which can only be detected by the original sampling of the dataset. Furthermore, it is necessary to specify a detail that is relevant to the practical importance of these services. With the exception of the method described in [48], all other methods require knowledge of the consumption of individual equipment throughout the training period for a given user. The application of these methods therefore requires the installation of measuring devices for individual household appliances, and that these have been recording energy consumption throughout the training period. It is therefore debatable to define the application of these methods to the same user as Non-intrusive Load Monitoring. In the procedure proposed we use only information relating to the status of activation of the same equipment that can be acquired independently of the reading of consumption, in automatic or semi-automatic mode through interaction with the user. The _unseen_ case is certainly more interesting, where the model is applied to a user whose only known time series is the aggregate load, as well as the type and nominal consumption of the appliances to be monitored.