## 7 Conclusions  
In this paper we have presented a new methodology for Non Intrusive Load Monitoring and Disaggregation, based on the recognition of the activation states of household appliances. The approach is based on the observation that a sufficiently experienced user is capable of recognizing these activation states by examining an aggregate consumption plot and that a neural network like the one proposed can emulate that ability. Leveraging the latest techniques used for semantic image segmentation, we have introduced a model based on convolutional networks, applicable on datasets with low sampling rate. The space of the features used for the classification has been enriched through the introduction of a \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] & 0.30 & 0.41 & 0.45 & 0.35 & - & 73 & 0.37 \\ Neural-NILM FHMM [30] & 0.40 & 0.86 & 0.50 & 0.55 & - & 67 & 0.57 \\ Neural-NILM AE [30] & 0.85 & 0.88 & 0.90 & 0.87 & - & 26 & \(-\)0.38 \\ Neural-NILM Rect. [30] & 0.79 & 0.86 & 0.87 & 0.82 & - & 18 & \(-\)0.13 \\ Neural-NILM LSTM [30] & 0.72 & 0.77 & 0.81 & 0.74 & - & 36 & \(-\)0.25 \\ dAE [32] & - & - & - & 0.83 & 0.76 & - & - \\ seq2 seq [31] & - & - & - & - & - & 24.5 & 0.37 \\ seq2 point [31] & - & - & - & - & - & 20.9 & 0.12 \\ H-ELM [40] & 0.90 & 0.92 & 0.94 & 0.89 & - & 23 & - \\ Deep AE [33] & - & - & - & 0.93 & - & - & - \\ \hline TP-NILM & 0.89 & 0.85 & 0.91 & 0.87 & 0.80 & 17.03 & \(-\)0.05 \\ \hline \hline \multicolumn{6}{c}{**Dishwasher**} \\ \hline  & **Prec.** & **Rec.** & **Acc.** & **F1** & **MCC** & **MAE** & **SAE** \\ \hline Neural-NILM CO [30] & 0.06 & 0.67 & 0.64 & 0.11 & - & 74 & 0.62 \\ Neural-NILM FHMM [30] & 0.03 & 0.49 & 0.33 & 0.05 & - & 110 & 0.75 \\ Neural-NILM AE [30] & 0.29 & 0.99 & 0.92 & 0.44 & - & 24 & \(-\)0.33 \\ Neural-NILM Rect. [30] & 0.89 & 0.64 & 0.99 & 0.74 & - & 30 & \(-\)0.31 \\ Neural-NILM LSTM [30] & 0.04 & 0.87 & 0.30 & 0.08 & - & 168 & 0.87 \\ dAE [32] & - & - & - & 0.51 & 0.50 & - & - \\ seq2 seq [31] & - & - & - & - & - & 32.5 & 0.78 \\ seq2 point [31] & - & - & - & - & - & 27.7 & 0.65 \\ H-ELM [40] & 0.35 & 1.00 & 1.00 & 0.55 & - & 22 & - \\ Deep AE [33] & - & - & - & 0.80 & - & - & - \\ \hline TP-NILM & 0.79 & 0.84 & 0.99 & 0.81 & 0.81 & 33.07 & 0.06 \\ \hline \hline \multicolumn{6}{c}{**Washing machine**} \\ \hline  & **Prec.** & **Rec.** & **Acc.** & **F1** & **MCC** & **MAE** & **SAE** \\ \hline Neural-NILM CO [30] & 0.06 & 0.48 & 0.88 & 0.10 & - & 39 & 0.73 \\ Neural-NILM FHMM [30] & 0.04 & 0.64 & 0.79 & 0.08 & - & 67 & 0.86 \\ Neural-NILM AE [30] & 0.07 & 1.00 & 0.82 & 0.13 & - & 24 & 0.48 \\ Neural-NILM Rect. [30] & 0.29 & 0.24 & 0.98 & 0.27 & - & 11 & \(-\)0.74 \\ Neural-NILM LSTM [30] & 0.01 & 0.73 & 0.23 & 0.03 & - & 109 & 0.91 \\ dAE [32] & - & - & - & - & 0.68 & 0.69 & - & - \\ seq2 seq [31] & - & - & - & - & - & 10.1 & 0.45 \\ seq2 point [31] & - & - & - & - & - & 12.7 & 0.28 \\ H-ELM [40] & 0.10 & 1.00 & 0.84 & 0.43 & - & 21 & - \\ Deep AE [33] & - & - & - & 0.41 & - & - & - \\ \hline TP-NILM & 0.86 & 0.87 & 1.00 & 0.86 & 0.86 & 8.31 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison for the _unseen_ 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, seq2 seq, seq2 point, Deep AE), Recurrent Neural Networks (Neural-NILM LSTM) and Extreme Learning Machine (H-ELM). temporal pooling module that performs a pooling at different time scales of the features calculated in the first convolution levels, thus increasing the receptive field without compromising the temporal resolution of the output. The approach is based on the multilabel classification of simultaneous active loads, and estimates the consumption of appliances as a constant average value during activation.