# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis
### Experiments for Validating the Basic Abilities of the Pretrained NILM Model  
To validate that the pretrained model can support continual learning despite its lightweight design, basic experiments were conducted on load identification and novelty detection, comparing it with the representative methods. For load identification, the compared methods included the following: (1) 2 DCNN with a colorful V-I trajectory as input [5], which is the most typical method. (2) The method incorporating an adaptive weighted recurrence graph (AWRG) of appliances into the backpropagation of 2 DCNN [23], which has gained significant attention. (3) The method embedding image-based representation into CNN, denoted as LRG [37], which has the best performance currently. For novelty detection, the compared methods were based on the output of the proposed feature extraction module, including the following: (1) The Siamese network, which calculates sample similarity to determine novelty through a neural network and shows outstanding performance [20]. (2) The method that adaptively detects new data based on density using DBSCAN [19]. (3) The classical OC-SVM method for novelty detection, which has been used to determine whether an appliance belongs to fixed types [38]. The load identification experiment was conducted on four public datasets using ten-fold cross-validation. In the novelty detection experiment, the eight appliance types with the highest sample counts across four datasets--namely hair dryers, fans, air conditioners, incandescent lamps, vacuum cleaners, fluorescent lamps, microwave ovens, and laptops (totaling 2350 samples)--were designatedas known classes. These commonly available household appliances were assigned a label of 0 due to their prevalence and suitability for establishing baseline data. Conversely, the remaining forty-eight appliance types (2085 samples) were treated as infrequent novel devices that were labeled 1. The dataset was partitioned into 8:2 training-testing splits, with experiments conducted in ten repeated trials to mitigate randomization effects.  
The average performance of the basic load identification experiment is shown in Table 1. LRG performs best on the PLAID and COOLL datasets. AWRG performs best on the HOUIDI and COOLL datasets. In comparison, 2 DCNN has the worst performance, probably due to the fact that it does not include information about the active current. The proposed method not only achieves the best performance on the WHITED dataset but also performs well on all datasets except PLAID when compared to the other methods. This is because the appliance patterns in these datasets are more homogeneous, making it easier to obtain optimal parameters using the analytic solution in (7). Notably, the proposed method requires only 0.69 MB of memory, significantly less than 2 DCNN's 436 MB and AWRG's 2.55 MB. Although slightly larger than LRG's 0.5 MB, it uses only 29.56% of LRG's floating-point operations while delivering comparable performance to the methods with more complex inputs and architectures.  
The performance of the basic novelty detection experiment is shown in Table 2. The Siamese network performs best, while DBSCAN and OC-SVM perform poorly. Especially, OC-SVM may fail due to the curse of dimensionality. It is worth noting that the Siamese network's outstanding performance comes at the cost of high computational complexity, as it requires storing all training samples and matching each input sample to the training samples one by one. In contrast, the proposed method avoids storing the original data, reducing the risk of data leakage, and requires only one forward propagation to detect new appliances. Even without the original data, the proposed method's accuracy is only about 6% lower than the Siamese network's and is significantly higher than that of other methods. Thus, the proposed method retains its advantages in real-world applications.