# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 2 Related Work: The Importance of Fraud Detection and Existing Approaches
Technical losses, which come from the conversion of electrical energy into heat through the Joule effect and other physical processes in the grid, account for a sizable amount of these losses, even though not all of them are attributable to theft or illegal use. Commercial losses resulting from theft or illicit activity, however, continue to be a major problem in Romania and have a big influence on the energy industry. Commercial losses made for 1.65% of all power losses in 2023, underscoring persistent issues in this sector. The findings of the inspection teams' campaign for the Delgaz Grid Distribution Company [23] and E.ON Romania [24] in Iasi County, which produced improved detection based on natural gas and electric energy, served as the basis for this study. The current initiative, in response to the rising number of unauthorized interventions due to increased energy prices, builds upon findings from field inspections carried out between January and March 2024. These inspections, primarily focused on natural gas, uncovered three instances of unauthorized electricity consumption among 85 inspected customers. The area studied is located in Iasi County, a region where infrastructure and GPS limitations present challenges, resulting in a low number of physical inspections. Despite these constraints, the inspection campaign--although not primarily designed to target electricity fraud--revealed a fraud rate of 3.53%, serving as a crucial reference for enhancing our verification methodology. This involves applying advanced data mining techniques, anomaly detection, data aggregation, and data fusion. Given this initial finding, a more comprehensive analysis is undertaken. We expanded our dataset to include 1872 cases of electricity fraud detected in Iasi County between 2017 and 2023. A fraud pattern identified in this broader dataset points to the tampering with or removal of metrological seals--a key indicator of fraudulent activity. As fraud detection plays a vital role in energy distribution, traditional inspection-based methods often fall short due to their high costs and inefficiency. To overcome these challenges, recent advancements in data-driven approaches and machine learning techniques have greatly enhanced the accuracy and effectiveness of fraud detection. The following section explores state-of-the-art machine learning and data mining techniques employed for this purpose. Table 1 provides a comparative analysis of existing fraud-detection approaches in the literature. While previous studies have achieved notable success in anomaly detection, most methods lack real-world validation, focus only on smart meters, or do not integrate multiple fraud-detection techniques. Our proposed methodology addresses these gaps by utilizing a hybrid ML approach applied to a large-scale dataset. The dataset used in this study consists of real-world electricity consumption records collected from E.ON Romania, covering a total of 38,500 clients, including both residential and commercial users. The dataset spans a period from 2017 to 2023, ensuring sufficient historical depth for pattern analysis. It includes a total of 1872 confirmed fraud cases, verified through field inspections, along with consumption records from non-fraudulent customers for comparison. Each record contains multiple attributes, including billing amounts, consumption trends, geographical location, smart meter data (where available), and inspection results. The dataset has been carefully selected to prioritize fraud detection scenarios, ensuring a high representation of fraudulent cases essential for training supervised learning models. Instead of relying solely on random sampling, our approach integrates anomaly detection, clustering, and time-series analysis to enhance the dataset's effectiveness in detecting fraudulent behavior while minimizing false positives. The dataset from E.ON Romania consists of 38,500 clients, including 1872 confirmed fraud cases identified through field inspections.