# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis
### Experiment for Validating the Proposed SCL-Based Pretraining Strategy  
The above experiments show that the proposed method significantly outperforms the existing continual learning-based NILM methods without storing the original data. Based on the experiments in Sections 4.4 and 4.5, this section strengthens the claim of the necessity of the proposed SCL strategy. The complete pretraining loss consists of cross-entropy (CE) loss and SCL loss, with both Gaussian (G) and random wandering (RW) noise used for data augmentation, as described in Section 3.3. It is compared with three alternatives: pure cross-entropy (CE), CE combined with SimCLR (an unsupervised contrastive loss) [26], and CE combined with SCL loss using only Gaussian noise (CE + SCL(G)). Comparing CE with CE + SCL(G + RN) directly verifies the necessity of SCL. Comparing CE + SimCLR with CE + SCL(G) demonstrates the effectiveness of supervised contrastive loss. Comparing CE + SCL(G) with CE + SCL(G + RN) serves as an ablation study for random wandering noise.  
The results are shown in Figure 5. In load identification, the performance of CE + SCL(G + RN) is close to the optimal performance from joint training on \(D_{0}\) to \(D_{16}\), verifying the effectiveness of analytic continual learning. Pretraining with CE + SCL(G + RN) losses improves performance on the dataset \(D_{0}\), as the feature space with greater distinctiveness between appliance types better satisfies the linear separability assumption for  
Figure 4: The average performance of continual learning-based methods in novelty detection: (**a**) ACC and (**b**) F1-macro.  
analytic continual learning. Moreover, the performance of the load identification model becomes more stable as the appliance types gradually increase. The final result shows a 1.8% improvement in the ACC and F1-macro compared to only using CE. In novelty detection, the CE + SCL(G + RN) offers greater advantages in this challenging continual learning task. In the initial rounds, a small number of appliances with the same operating mode (but mislabeled as unlearned types) are mapped to similar regions due to the enhanced feature representation from CE + SCL(G + RN)-based training. This label noise [4] leads to overly optimistic performance during training without the SCL strategy. In the continual learning process of novelty detection, the SCL-based pretraining strategy enhances feature space distinctiveness, making learned appliances less likely to be forgotten and improving overall stability. The final results show that CE + SCL(G + RN) improves the ACC by 32% and F1-macro by 18% compared to CE. If only the Gaussian noise is used for data augmentation in SCL, the performance is still better than CE, but after all the new appliance datasets are learned, identification and novelty detection accuracy are 1.1% and 14.8% lower, respectively. For unsupervised contrastive learning, SimCLR does not leverage the label information to enhance inter-type separation and intra-type clustering. In appliance identification, the model capacity increases with each new appliance, so the poor initial point from SimCLR pretraining has little impact on analytic continual learning. However, for novelty detection, where model capacity remains fixed, SimCLR-pretrained models tend to map almost all samples inside the hypersphere, classifying them as known appliances. Notably, as new datasets are added, the model's accuracy increases in novelty detection simply because most samples are classified as known. When unknown samples are relabeled as known, the accuracy rises accordingly.