## 6 Conclusions and Future Works  
In this paper, we proposed a two-stage power consumption predictor and one-class classifier for detecting electricity theft in the smart grid while optimizing data collection through the use of CAT data collection approach. Initially, we developed a global-based predictor trained solely on benign CAT consumption readings. The MAAPE values without the use of the CAT approach were 3.65 for the LSTM, 5.19 for the transformer, and 2.22 for the combined LSTM and transformer models. As the CAT threshold was increased from 10% to 30%, the MAAPE ranged from 3.93 to 4.71 for the LSTM, 5.44 to 8.2 for the transformer, and 3.17 to 3.61 for the combined models. Implementing the CAT approach resulted in significant transmission savings: 20.9% at a 10% threshold, 44.9% at 20%, and 64.8% at 30%. To enhance prediction accuracy, we proposed a cluster-based predictor, grouping consumers with similar consumption patterns. The MAAPE values for the cases when actual readings are used, global-based CAT, and cluster-based CAT were 3.65, 3.93, and 3.21 for the LSTM model; 5.19, 5.44, and 5 for the transformer model; and 2.22, 3.17, and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **OC-SVM** & **Autoencoder + OC-SVM** \\ \hline Accuracy & 89.2 & 95.6 \\ \hline Precision & 91 & 92.2 \\ \hline Recall & 86 & 99.8 \\ \hline F1 & 88.7 & 96.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluating the performance of the one-class classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **OC-SVM** & **Autoencoder + OC-SVM** \\ \hline TP & 153,741 & 188,663 \\ \hline FP & 15,132 & 15,937 \\ \hline FN & 25,469 & 377 \\ \hline TN & 190,308 & 189,503 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the one-class classifier. 2.96 for the combined models, respectively. In the second stage, we trained an autoencoder and an OC-SVM using benign reconstruction errors from the predictor to detect electricity theft. This approach demonstrated superior performance, achieving accuracy, precision, recall, and F1 scores of 95.6, 92.2, 99.8, and 96.4, compared to 89.2, 91, 86, and 88.7 for the OC-SVM. A possible limitation of the clustering-based method is the need for more computational resources to cluster data and training a model for each cluster instead of training only one model for all consumers. Given the high accuracy of our model, this extra computation can be justified. This paper does not address privacy preservation. In practice, power consumption readings can reveal sensitive information about consumers, such as the appliances they are using, their sleep patterns, and whether they are traveling. These privacy concerns may discourage consumers from sharing their data, which is necessary to train machine learning models. To overcome this limitation, we plan to explore privacy-preserving techniques, such as federated learning, for training our model. Additionally, this paper does not examine adversarial attacks, such as Trojan attacks. In a Trojan attack, the model is trained on specially crafted data to introduce vulnerabilities. As a result, the model performs well on clean data but misclassifies data containing a trigger. This vulnerability could allow attackers to steal electricity undetected. In our future work, we will investigate this type of attack to enhance the security of our classifier. Finally, in smart grid net metering systems, renewable energy generators (such as solar panels) are installed at consumer locations. When there is surplus energy production, the system injects power into the grid for monetary incentives, and consumers draw power from the grid when there is a shortage. Unlike the consumption metering system considered in this paper, net meters report the difference between the power consumed and the power injected, which can result in either a positive or negative value. In future work, we will explore applying our proposed approach to this system. Conceptualization, A.B., M.M., M.A. ([PERSON]), M.A.