# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis
### Experiment for Validating Analytic Continual Learning-Based Method in Load Identification  
To validate the proposed continual learning-based NILM method in load identification, appliance types with over 200 samples across four public datasets are designated as common types, forming the dataset \(D_{0}\) for pretraining the model. For the remaining appliances, a random set of every 3 types forms the streaming dataset \(D_{k}\); that is, a total of 16 rounds  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**PLAID**} & \multicolumn{2}{c}{**WHITED**} & \multicolumn{2}{c}{**HOUIDI**} & \multicolumn{2}{c}{**COOLL**} \\ \cline{2-9}  & **ACC** & **F1-Macro** & **ACC** & **F1-Macro** & **ACC** & **F1-Macro** & **ACC** & **F1-Macro** \\ \hline LRG & **0.989** & 0.968 & 0.987 & 0.981 & 0.965 & 0.912 & **0.999** & **0.998** \\ AWRG & 0.978 & 0.955 & 0.979 & 0.975 & **0.998** & **0.993** & **0.999** & **0.998** \\
2 DCNN & 0.947 & 0.936 & 0.986 & 0.982 & 0.902 & 0.833 & 0.973 & 0.964 \\ Ours & 0.968 & **0.970** & **0.996** & **0.993** & 0.996 & 0.992 & 0.999 & 0.998 \\ \hline \hline \end{tabular}  
* **Bold** (optimal in vertical comparison).  
\end{table}
Table 1: The performance of the basic load identification experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Siamese Network** & **DBSCAN** & **OC-SVM** & **Ours** \\ \hline ACC & **0.875** & 0.649 & 0.595 & 0.823 \\ F1-macro & **0.871** & 0.579 & 0.532 & 0.816 \\ Without original data & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline \hline
**Bold** (optimal in horizontal comparison); \(\surd\) (no original data required); \(\times\) (original data required). \\ \hline \hline \end{tabular}  
\end{table}
Table 2: The performance of the basic novelty detection experiment.  
of continual learning tasks are set up. For each appliance type, 80% of the samples are randomly selected for training, with the remaining used for testing. The following methods are used for comparation: (1) Freezing the feature extraction module and fine-tuning only the load identification branch as a baseline method without continual learning. (2) On the basis of freezing and then fine-tuning, DBSCAN-based typical waveform selection is added, and new data are jointly trained with the original data [11], denoted as TSR. The maximum number of stored samples per appliance type is set to 30. (3) The representative class-incremental learning method, incorporating knowledge distillation and output layer weight alignment in fine-tuning and simplifying waveform selection, based on average distance [14], is denoted as CIL. The maximum number of total stored samples for this method is set to 3000.  
The performance of continual learning-based load identification is shown in Figure 3. Notably, the baseline method declines rapidly in performance. During the learning of \(D_{1}\), it catastrophically forgets appliances from \(D_{0}\) (the ACC drops from 0.99 to 0.07), and the loss of basic knowledge further hinders the learning of new appliances. This highlights the need to prevent catastrophic forgetting. After 16 rounds of continual learning, with the number of appliance types reaching 56, the ACC and F1-macro of the proposed method remain above 0.95 and 0.94, close to the performance of joint training with all the data. During the continual learning process, the proposed method degrades slowly and significantly outperforms the compared methods without requiring the storage of original data. In contrast, the two methods that store original data are less effective at mitigating catastrophic forgetting. As appliance types with fewer patterns arrive, TSR performs better and shows a slight increase in F1-macro by storing typical waveforms. In summary, the proposed method takes the practicality of continual learning-based load identification to a new height.