# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 4 Experiment and Result Analysis
### Performance Comparison of Algorithms  
In this section, the performance of proposed classification algorithm is compared with some existing algorithms by the parameters of precision, recall and F-measure. In order to calculate the classification accuracy, the results of the proposed algorithm and unsupervised GSP-based approach [31] on house 1, 2 and 6 are represented in Table 6. It demonstrates that the proposed method has better performance in classification accuracy compared with GSP-based method for all houses. Furthermore, the performance of proposed MFIC algorithm is compared with the state-of-the-art NILM approaches used for low sampling rate and power signals. The F-measure values of MFIC, the combined k-means/SVM classification [35], the HMM-based method and the decision tree (DT) approach [17] are denoted as \(F_{U}\), \(F_{S}\), \(F_{H}\), and \(F_{DT}\), respectively. The comparison results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**House** & **Load** & **P\({}_{\textbf{c}}\)(Proposed)** & **R\({}_{\textbf{c}}\)(Proposed)** & **F\({}_{\textbf{c}}\)(Proposed)** & **P\({}_{\textbf{c}}\)(GSP)** & **R\({}_{\textbf{c}}\)(GSP)** & **F\({}_{\textbf{c}}\)(GSP)** \\ \hline \multirow{4}{*}{**House 1**} & Washer dryer & 0.99 & 0.89 & 0.95 & 0.95 & 0.63 & 0.78 \\  & Oven & 0.99 & 0.78 & 0.86 & 0.95 & 0.49 & 0.58 \\  & Bathroom & 0.99 & 0.95 & 0.98 & 0.8 & 0.64 & 0.61 \\  & Dishwasher & 0.99 & 0.93 & 0.95 & 0.95 & 0.21 & 0.39 \\  & Refrigerator & 0.99 & 0.82 & 0.93 & 0.98 & 0.71 & 0.83 \\ \hline \multirow{4}{*}{**House 2**} & Kitchen & 0.99 & 0.99 & 0.99 & 0.92 & 0.87 & 0.9 \\  & outlets & & & & & & \\  & Microwave & 0.95 & 0.99 & 0.99 & 0.96 & 0.88 & 0.92 \\  & Refrigerator & 0.98 & 0.95 & 0.98 & 0.87 & 0.78 & 0.8 \\  & Dishwasher & 0.98 & 0.99 & 0.99 & 0.65 & 0.39 & 0.38 \\  & Oven & 0.98 & 0.99 & 0.99 & 0.28 & 0.9 & 0.41 \\ \hline \multirow{4}{*}{**House 6**} & Bathroom & 0.98 & 0.96 & 0.97 & & & \\  & Refrigerator & 1 & 0.94 & 0.96 & 0.9 & 0.78 & 0.82 \\ \cline{1-1}  & Air & 0.99 & 0.99 & 0.99 & 0.99 & 0.79 & 0.9 \\ \cline{1-1}  & conditioner & & & & & & \\  & Stove & 0.97 & 0.65 & 0.81 & 0.65 & 0.5 & 0.6 \\ \cline{1-1}  & Kitchen & & & & & & \\  & outlets & 0.98 & 0.98 & 0.98 & 0.52 & 0.51 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance comparison with graph signal processing (GSP)-based method for Houses 1, 2, 6. Figure 9: Comparison of actual and estimated percentage of contribution in energy consumption (_PCEC_) values for House 1, 2, 6. On the one hand, we study the variation of performance with respect to different appliances, mainly including some controllable or high-power loads in the REDD database. It can be seen that the MFIC algorithm achieves the best disaggregation in terms of F-measure for all appliances. HMM yields significantly have worse results, but it usually performs well in identifying refrigerator because the continuity and singleness (i.e., no other devices operates, especially at night) of its operation bring sufficient data for training. The results of k-means/SVM and DT perform better but worse than those of MFIC. On the other hand, the average results for three REDD houses are compared. The results of combined k-means/SVM classification and HMM are shown in [31], and the results of DT are reported in [36]. The proposed MFIC algorithm has consistently high performance across all three houses and outperforms other algorithms in both House 1 and House 2. The combined k-means/SVM method shows a higher accuracy in House 6. Moreover, we use the disaggregation accuracy metric to compare the performance of MFIC algorithm with the Bayesian HMM [21], segmented integer quadratic constraint programming (SIQCP) [23], sparse coding (SC), and discriminating sparse coding (discSC) [25].