# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 5 Conclusions  
To address the continual emergence of new appliances, a NILM method based on analytic continual learning is proposed, eliminating the complexity and data leakage risk of the existing methods that require storing original appliance data. The designed NILM model consists of a 1D depthwise separable convolution-based feature extraction module and dual output branches for load identification and novelty detection. It is further optimized for appliance-type distinctiveness in the feature space by the proposed SCL-based pretraining strategy. When new appliance data arrive, the novelty detection branch first determines whether learning is required. The analytic continual learning method then updates the load identification and novelty detection branches using only new data, realizing a closed-loop iteration of novelty detection-continual learning in streaming load data. The experimental results show that optimizing the learning process with the proposed method enables the NILM model to match the best performance of existing load identification and novelty detection methods while reducing the computational effort by 70.44%. Moreover, the proposed method exhibits a more stable continual learning ability when appliance types are added when compared to the methods that need to store original data. As appliance types are added from 8 to 56, the accuracy of load identification and novelty detection is 13.52% and 15.33% higher than the best existing method. In addition, the proposed method is deployed on an STM32F407-based smart socket, meeting real-world  
Figure 7: The average performance in hardware deployment: (**a**) load identification (ACC); (**b**) load identification (F1-macro); (**c**) novelty detection (ACC); (**d**) novelty detection (F1-macro).  
NILM performance requirements within an acceptable execution time. Overall, a novel and practical solution is presented for continual learning in NILM.  
In the future, we will focus on the following challenges: (1) Improving novelty detection performance by exploring strategies such as dynamically expanding the model's capacity as the number of appliance types increases. (2) Combining sample selection methods from active learning [39] to reduce the amount of labeling required for new appliances. (3) Promoting the application of smart sockets by deploying the proposed method to validate it across a wider variety of appliances.  
Conceptualization, C.L. and Q.L.; methodology, C.L. and Q.L.; software, M.L. and W.G.; validation, C.L., Q.L. and T.Y.; formal analysis, M.L.; investigation, C.L.; resources, M.L.; data curation, C.L.; writing--original draft preparation, C.L. and Q.L.; writing--review and editing, T.Y. and Z.P.; visualization, Q.L.; supervision, T.Y. and Z.P.; project administration, T.Y.; funding acquisition, T.Y. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the National Natural Science Foundation of China (52207105, U24B6010) and the Guangdong Basic and Applied Basic Research Foundation (2023A1515011598).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.