# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 4 Result
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Method** & **Air** & **Fridge** & **Microwave** & **Washing Machine** & **Dish Washer** \\ \hline \multirow{6}{*}{MAE} & KNN & 38.484 & 34.014 & 6.928 & 6.677 & 10.630 \\  & DAE & 36.964 & 39.520 & 17.015 & 12.081 & 25.107 \\  & CNN s-s & 61.129 & 38.413 & 9.973 & 18.497 & 19.084 \\  & CNN s-p & 39.635 & 13.760 & 13.155 & 11.959 & 11.624 \\  & MSA-Resnet & 36.388 & 10.440 & 4.862 & 2.161 & 2.013 \\ \hline \multirow{6}{*}{SAE} & KNN & 0.0006 & 0.026 & 0.060 & 0.323 & 0.121 \\  & DAE & 0.0001 & 0.071 & 2.317 & 2.835 & 1.405 \\ \cline{1-1}  & CNN s-s & 0.013 & 0.051 & 0.060 & 3.925 & 0.886 \\ \cline{1-1}  & CNN s-p & 0.006 & 0.074 & 0.319 & 2.467 & 0.098 \\ \cline{1-1}  & MSA-Resnet & 0.014 & 0.025 & 0.143 & 0.152 & 0.052 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of load disaggregation index of Household 25 of the WikiEnergy dataset. In order to prove the effectiveness of the Leaky-Relu function, under the same conditions, comparative experiments are conducted with WikiEnergy's Household 25 using the Relu function. According to the experimental results in Table 3, at the two indicators of the \(MAE\) and the \(SAE\), the algorithm using the Leaky-Relu function is better. For further verification, we selected five electric appliances from Household 5 in the UK-DALE dataset for additional experiments. Figure 9 shows the results of disaggregation. The figure shows that all of the above algorithms are able to achieve effective disaggregation for the kettle, an electrical appliance that is used often. For the fridge, the KNN and the DAE work worse than the CNN s-s, the CNN s-p, and the MSA-Resnet. For the microwave, the washing machine, and the dishwasher, which are infrequently used and have a low power consumption, the MSA-Resnet has better disaggregation results than the other two deep learning algorithms, mainly because it could better detect peaks and state changes. Table 4 shows Household 5's load disaggregation evaluation index in the UK-DALE dataset. Table 4 shows that the MSA-Resnet does better in \(MAE\) and \(SAE\) compared with other methods. For the \(MAE\), the MSA-Resnet performs better with respect to the kettle, the fridge, the washing machine, and the dishwasher. The MSA-Resnet has smaller \(SAE\) values in the kettle, the fridge, and the washing machine. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Index** & **Method** & **Air** & **Fridge** & **Microwave** & **Washing Machine** & **Dish Washer** \\ \hline \multirow{4}{*}{Recall} & KNN & 0.998 & 0.996 & 0.759 & 0.290 & 0.561 \\  & DAE & 0.999 & 0.996 & 1 & 0.451 & 0.833 \\  & CNN s-s & 0.999 & 0.990 & 0.949 & 0.290 & 0.868 \\  & CNN s-p & 0.999 & 1 & 0.987 & 0.129 & 0.596 \\  & MSA-Resnet & 1 & 0.986 & 0.880 & 0.806 & 1 \\ \hline \multirow{4}{*}{Precision} & KNN & 0.987 & 0.870 & 0.198 & 0.236 & 0.336 \\  & DAE & 0.987 & 0.853 & 0.050 & 0.229 & 0.281 \\  & CNN s-s & 0.939 & 0.847 & 0.033 & 0.428 & 0.391 \\  & CNN s-p & 0.995 & 0.996 & 0.050 & 0.047 & 0.414 \\  & MSA-Resnet & 0.999 & 0.988 & 0.795 & 0.962 & 0.884 \\ \hline \multirow{4}{*}{Accuracy} & KNN & 0.991 & 0.889 & 0.967 & 0.993 & 0.978 \\  & DAE & 0.991 & 0.872 & 0.812 & 0.992 & 0.967 \\  & CNN s-s & 0.958 & 0.864 & 0.729 & 0.995 & 0.978 \\  & CNN s-p & 0.997 & 0.980 & 0.816 & 0.986 & 0.982 \\  & MSA-Resnet & 0.999 & 0.981 & 0.997 & 0.999 & 0.998 \\ \hline \multirow{4}{*}{F1} & KNN & 0.993 & 0.928 & 0.314 & 0.260 & 0.421 \\  & DAE & 0.993 & 0.919 & 0.095 & 0.304 & 0.421 \\ \cline{1-1}  & CNN s-s & 0.968 & 0.913 & 0.064 & 0.346 & 0.539 \\ \cline{1-1}  & CNN s-p & 0.997 & 0.986 & 0.096 & 0.069 & 0.489 \\ \cline{1-1}  & MSA-Resnet & 0.999 & 0.987 & 0.835 & 0.877 & 0.938 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different algorithms for electrical on/off judgement of Household 25 in the WikiEnergy dataset.