# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 4 Result  
This experiment used the Keras neural network framework. The computer processor was AMD2600, and the graphics card was 1060 6G. After data was standardized, the length of the sliding window was set to 200, the learning rate of the network was set to 0.001, and the Adam optimizer was selected as the network optimizer. [PERSON]'s experiments indicate that the DAE algorithm performs well in NILD, and [PERSON]'s work also shows a good performance of CNNs in sequence-to-sequence and sequence-to-point load disaggregation. From the WikiEnergy data, we selected the air conditioner, fridge, microwave, washing machine, and dishwasher from Household 25. From the UK-DALE dataset, the kettle, fridge, microwave, washing machine, and dishwasher of Household 5 were selected. In order to verify the effectiveness and stability of the algorithm proposed in this paper, four approaches were compared with the MSA-Resnet: the KNN, the DAE, the CNN sequence-to-sequence learning (CNN s-s), and the CNN sequence-to-point learning (CNN s-p). Firstly, the WikiEnergy dataset was tested. Figure 7 shows the disaggregation effect diagrams of five appliances of WikiEnergy from Household 25, and the actual power consumption  
Figure 6: Sliding operation. data of these appliances. The figure compares the four disaggregation methods with the MSA-Resnet proposed in this paper. In order to verify the effectiveness of the proposed method, two evaluation indexes were selected to evaluate the performance of the algorithm: the Mean Absolute Error (\(MAE\)) and the Signal Aggregate Error (\(SAE\)). The \(MAE\) evaluation index was used to measure the average error of power consumption and the actual power consumption of individual electrical appliances disaggregated at each moment. The \(MAE\) is expressed as the following:  
\[MAE=\frac{1}{T}\sum_{T}^{t=1}\left|p_{t}-g_{t}\right|, \tag{13}\]  
where \(g_{t}\) represents the actual power consumed by an appliance at time \(t\), \(p_{t}\) represents the disaggregation power of the appliance at time \(t\), and \(T\) represents the number of time points. Equation (14) is the expression of the \(SAE\), where \(\hat{e}\) and \(e\) represent the power consumption predicted by disaggregation within a period of time and the real power consumption within a period of time. This index is helpful for daily electricity reports. \[SAE=\frac{\left|\hat{e}-e\right|}{e}. \tag{14}\]  
Figure 7 describes disaggregations of Household 25 in the WikiEnergy dataset. It can be seen that the above algorithms can basically achieve effective load disaggregation for the air conditioner. In the load disaggregation diagram of the fridge, the DAE and CNN s-s algorithms fluctuate greatly in the mean area of the appliance, compared with other algorithms. The KNN algorithm has the worst load disaggregation effect on the last three kinds of electrical appliances, so it could not realize an effective disaggregation of mutation points. For these three low-frequency electrical appliances, the load disaggregation of CNN s-s and CNN s-p algorithms are stable compared with the other two algorithms, but the load disaggregation of the CNN s-p method fluctuates greatly in the region of low power consumption. In summary, compared with other methods in load disaggregation, the MSA-Resnet shows the best performance on each electrical appliance, based on the power consumption curve. Figure 7: Comparison load disaggregation results of Household 25 in the WikiEnergy dataset. Table 1 shows comparisons of \(MAE\) and \(SAE\) indexes of Household 25 load disaggregation in the WikiEnergy dataset. It can be seen that MSA-Resnet has obvious advantages in the disaggregations of the air conditioner, fridge, microwave, washing machine, and dishwasher. According to the \(MAE\) index, the MSA-Resnet performs better than the other four methods. For the \(SAE\), the MSA-Resnet achieves the lowest value on the fridge, washing machine, and dishwasher, and accurate disaggregation of energy is achieved over a period of time.