## 4 Proposed Scheme
### Dataset Preparation for CAT Approach  
To train and evaluate our power consumption prediction and electricity theft detection models, we used a publicly available dataset comprising real-world electricity consumption data from honest users. This dataset, released by the Irish Electric and Sustainable Energy Authority in January 2012, offers a rich collection of fine-grained power consumption readings. It is a large and diverse dataset that includes data from over 3000 consumers, spanning a substantial time frame of more than 536 days. This reflects diverse energy consumption characteristics across different regions, consumer profiles, seasons, and potential methods of electricity theft. The readings inside this dataset were collected at 30 min intervals, resulting in a comprehensive record of electricity usage for each consumer. This extensive dataset of benign consumption patterns serves as a valuable resource for both training and evaluating our models' capability to predict future readings and detect electricity theft [56]. Additionally, the dataset facilitates the simulation of a distribution system, where electricity is distributed from substations to consumers.  
Since this dataset contains actual readings, we used it to generate datasets specifically tailored for the CAT data collection approach. These new datasets incorporated various thresholds to determine when a reading is transmitted by the smart meter, including 10%, 20%, and 30%. For instance, with a 10% threshold, the smart meter transmits a reading only if the absolute difference in consumption compared to the previously reported reading exceeds 10%. We evaluated the data transmission efficiency achieved by the CAT approach using the following equation, which calculates the ratio of readings transmitted under CAT to the total number of readings across different transmission scenarios as follows:  
\[\eta=\frac{R_{p}-R_{c}}{R_{p}}\times 100 \tag{2}\]  
where \(R_{c}\) represents the number of readings transmitted using the CAT approach, which transmits only when consumption changes exceed a threshold. Conversely, \(R_{p}\) denotes the total number of readings that are transmitted using periodic transmission, where readings are sent at regular intervals regardless of consumption changes. As the value of the threshold increases, the probability of a consumption change surpassing the threshold and prompting a transmission decreases, resulting in fewer transmissions but with a bigger error in the readings. Conversely, shorter transmission intervals reduce the savings, as the increased frequency of intervals raises the likelihood of consumption changes exceeding the threshold, leading to more frequent transmissions but with smaller errors in the readings. This dynamic underscores a trade-off between the efficiency of data transmission and the amount of error introduced in the readings due to using the CAT data collection approach.  
In the following example, we further explain how the CAT readings are obtained. In this simplified illustration, four electricity consumption readings--10.0, 10.5, 11.3, and 12.2--are presented to demonstrate how the CAT approach operates under a 10% threshold. Typically, there would be 48 readings per day over 536 days; however, this concise example serves to clarify the transmission process. Initially, the reading of 10.0 is transmitted as thefirst recorded value. The next reading, 10.5, represents a 5% increase from the previously transmitted value, which is below the 10% threshold, so no transmission occurs. When the consumption reaches 11.3, the 13% change from the last transmitted reading (10.0) exceeds the threshold, resulting in its transmission. The final reading, 12.2, reflects a 7.96% change from 11.3, which remains below the threshold, so this reading is not transmitted. When no transmission occurs, the utility records the last transmitted value as the current one. Thus, the recorded values at the utility will be 10.0, 10.0, 11.3, and 11.3, while the transmitted values are 10.0 and 11.3.