Electrical Theft Detection Using CNN Algorithm

The original data set consists of 93% regular users and 7% abnormal users. The model obtained an accuracy of 94% and an average precision of 92%. However, due to the limited quantity of abnormal users in the dataset, the recall was only 68%. This indicates that the model can accurately identify the majority of abnormal consumers but may miss some instances. Despite this, the overall performance of the model is good, with high accuracy and precision. In conclusion, CNNs can be effective for modeling electrical theft. These algorithms can learn complex patterns and features in the data, allowing them to accurately identify instances of electrical theft. In this way, CNNs can help utility companies and other organizations detect and prevent instances of abnormal electricity consumption, which can help reduce losses and improve the efficiency of electricity distribution.