## 1 Introduction  
The concept of smart grids leverages recent advancements in sensing, communication, artificial intelligence, and data processing to transform the electricity grid by enabling the intelligent monitoring, control, and management of power generation and distribution [1]. A key component of the smart grid is advanced metering infrastructure (AMI) [2]. As depicted in Figure 1, an AMI network features smart meters (SM) installed at consumer premises, connected via wireless communications to a gateway that links the meters to a system operator. This setup allows for the collection of detailed power consumption readings (e.g., every few seconds) and the transmission of these data to the system operator for optimal power management, load prediction, and dynamic pricing billing. However, the extensive scalability of the network raises concerns about the cost of collecting such large volumes of data. A promising technique for efficient data collection is known as Change and Transmit (CAT). Periodically transmitting power consumption readings at regular intervals, such as every few seconds, generates a large volume of data, leading to the inefficient use of communication bandwidth and other resources. When cellular networks are used to transmit data from smart meters, the cost becomes significant. As the network expands to millions of SMs, scalability challenges arise. The CAT approach addresses this issue by transmitting new power consumption readings only when there is enough change from the previous reading. This method reduces the bandwidth and costs associated with data collection by avoiding unnecessary data transmission [3; 4]. Studies show that transmission savings range from 22% to 78% when the transmission rate is one reading per minute, and a new reading is only sent with at least a 10% change in consumption [3]. However, these benefits come with a trade-off. When a reading is not transmitted, the system operator relies on the last reported reading, introducing an error range determined by the transmission threshold. This makes it challenging to produce highly accurate power consumption predictions and detect electricity theft using these less precise readings. AMI networks face several challenges [5; 6; 7]. This paper focuses on two main issues. The first challenge is electricity theft. In a smart grid's AMI, SMs report electricity consumption readings to the electric utility using either wired or wireless transmission media. If these readings are altered during transmission, the malicious activity can be easily detected using data integrity checks, such as digital signatures. To evade detection, malicious consumers hack their SMs and manipulate the electricity consumption readings before transmission to reduce their electricity bills [8; 9; 10]. This fraudulent action not only results in financial losses but also degrades grid performance, as the readings are essential for energy management and load monitoring. The second challenge is designing an accurate predictor that can provide reliable power consumption forecasts despite the noisy input  
Figure 1: The main entities and the flow of data in a smart grid AMI network. from CAT readings. This study aimed to investigate these two challenges within the context of the CAT data collection technique. Several machine learning (ML) models have been proposed for detecting electricity theft, and experimental results demonstrate that machine learning is a promising technique. However, most existing papers utilize supervised learning [11; 12; 13; 14], where models are trained on two classes: benign and malicious. These models are designed to detect specific attacks and their performance is unpredictable when encountering new, unseen attacks. Very few studies have explored the use of unsupervised learning [11; 15; 16], and none have addressed the problem in the context of the CAT data collection approach. Regarding power consumption prediction, numerous studies have aimed to create accurate predictors, but none have considered the CAT data collection approach.