# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 4 Experiment and Result Analysis
### Load Labeling and Identification  
The linear discriminant classifier group of House 2 is used to label each candidate model and the results of each classifier are reported in Table 5. The classifier group of House 2 consists of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & **Mon.** & **Tue.** & **Wed.** & **Thur.** & **Fri.** & **Sat.** & **Sun.** \\ \hline Positive power & 1874 & 1885 & 1872 & 1802 & 1677 & 1889 & 1889 \\ Negative power & \(-\)1887 & \(-\)1862 & \(-\)1823 & \(-\)1821 & \(-\)1686 & \(-\)1867 & \(-\)1867 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The pairing result of the ON/OFF model 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Date** & **Effective Detection Rate** \\ \hline Mon. & 0.97 \\ Tue. & 0.98 \\ Wed. & 0.98 \\ Thur. & 0.96 \\ Fri. & 0.98 \\ Sat. & 0.97 \\ Sun. & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The effective detection rate of the proposed algorithm. Figure 7: Results of event pairing for House 2.  
seven typical appliances with great adjustable potential, including kitchen outlets, stove, microwave, high-power state of dishwasher, low-power state of dishwasher, multi-state of dishwasher and refrigerator. In particular, different modes of dishwasher with different operation cycles are treated as separated appliances. As can be seen in Table 5, the models that are identified as appliance 4, 5 and 6 are all labeled as a dishwasher. From the signs of classifier value (greater than 0), model 1 is determined as appliance 3 while model 2 is appliance 4. Model 3 and model 4 and model 5 represent appliance 7, appliance 5 and appliance 6. If there is no positive value for a model, it means that the model is caused by an unregistered appliance in the feature database, which may be a new or low power consumption appliance not interested by users. For the detailed comparison between the disaggregated models and corresponding appliances, the power consumption of each model is reconstructed. The essence is to transform a predicted label into predictive power consumption. The actual and reconstructed power profiles of each appliance are illustrated in Figure 8. The three figures on the left show the power comparison in one day. It can be seen that the algorithm can accurately identify the operation state of each appliance. The power profiles within an interval are displayed on the right side. For the single-state load such as microwave, the power signal can be estimated quite accurately. To comprehensively verify the effectiveness of the proposed algorithm, the _PCEC_ values are shown in a schematic pie plot. The results of Houses 1, 2, 6 during one week are presented in Figure 9, which illustrates that some appliances in the feature database are OFF during the whole period and the proposed algorithm can detect this pattern accurately. Likewise, there is not any missing identification  
Figure 8: Ground truth (GT) and estimated appliances’ power consumption (D) for House 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Separated Model**} & \multicolumn{6}{c}{**Appliance Classifiers**} \\ \cline{2-7}  & **App 1** & **App 2** & **App 3** & **App 4** & **App 5** & **App 6** & **App 7** \\ \hline Mod 1 & \(-\)0.85 & \(-\)0.79 & **0.04** & \(-\)0.85 & \(-\)0.70 & 0 & \(-\)0.79 \\ Mod 2 & \(-\)0.62 & \(-\)0.74 & \(-\)0.38 & **0.06** & \(-\)0.56 & 0 & \(-\)0.70 \\ Mod 3 & \(-\)0.47 & \(-\)0.77 & \(-\)0.73 & \(-\)0.77 & \(-\)0.65 & 0 & 0.03 \\ Mod 4 & \(-\)0.34 & \(-\)0.55 & \(-\)0.45 & \(-\)0.60 & 0.10 & 0 & \(-\)0.58 \\ Mod 5 & & & & & & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Identification results of separated candidate models. of an appliance being OFF shown in the disaggregation result. The _PCEC_ values estimated by our method are closer to the ground truth, which further verifies the ability of the proposed algorithm in signal reconstruction. For the three houses, the average absolute differences between the results and the actually measured values are 3.97%, 0.30% and 0.73%, respectively.