*5.1. Performance Comparison*

The performance comparison between the proposed model and other models in three datasets is demonstrated in Table 4.

The proposed model performs better than other models in different training ratios, as shown in Table 4. Take the result of a 70% training ratio as an example. The proposed model has the highest PR and RR for each dataset. However, other models had better ARs in some dataset. For example, the AR of the 1D-CNN model was the highest for dataset (a)—4.3% higher than that of the proposed model. However, F1 (which is the most comprehensive indicator of the classification performance) of the proposed model was the highest in each dataset and reached 0.757, 0.850 and 0.904 in dataset (a), dataset (b) and dataset (c), which is 20.1%, 15.2% and 8.9% higher than the second-place model respectively. Meanwhile, the proposed model performed better with the increase in the training ratio. For example, in dataset (c), the F1 increased from 0.759 to 0. 896 as the training ratio increased from 50 to 80%.


**Table 4.** Results on different datasets with different models.

It is also worth noting that the proposed model had better universality and performance in the realistic dataset. Comparing dataset (c) with the realistic dataset (a) and dataset (b), the F1 of the proposed model with dataset (c) reached about 0.95, but 0.816 for dataset (a) and 0.896 for dataset (b) when the training ratio was 80%. This is mainly because that the electricity theft data in dataset (c) were artificially generated, of which the data features can be identified and extracted easily by machine learning models. However, realistic electricity theft data are more complicated and lack regularity. Therefore, the results in realistic datasets are relatively worse than those in dataset (c). However, compared with other models, the performance of the proposed model was still the highest.

The comparing results show that the proposed model has better performance overall, which implies that the proposed model has higher accuracy in electricity theft detection.
