# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses
#### 3.2.1 Energy Losses Caused by Fraud and Illegal Behavior  
Energy losses in electricity grids have a deleterious effect on the economy, as well as on system stability and reliability. In the domain of energy research, researchers have placed emphasis on distinguishing between the two categories of energy losses to facilitate the development of more effective methodologies for reducing losses. In a reference work, [PERSON] and [PERSON] [31] focused on the determination of energy theft. In this paper, they also addressed cybersecurity issues related to consumption data, which can be hidden or modified. To address these concerns, they have proposed a suite of advanced algorithms designed to identify and mitigate these threats. A non-technical loss detection algorithm, dubbed A-Star, has been developed, which also possesses the capability to localize the  
Figure 6: Proportion of types of studies conducted to identify non-technical losses. consumption site where anomalies are occurring. Another successfully applied method can be found in [32], where the authors also considered the objective of mitigating energy fraud through intelligent fraud detection. The implementation of techniques to recognize surveillance patterns using artificial neural networks (ANNs) and support vector machines is also discussed. Additionally, an optimal classifier was introduced to facilitate rapid non-technical loss recognition. As fraudulent activities related to the electrical energy system have become increasingly evident, there is a growing imperative to detect them in a more efficient way. In this regard, the authors of [33] have proposed a study in which they have implemented an algorithm to analyze very complex databases based on consumption data. The objective of this analysis is to achieve a better and more efficient detection process. To this end, the authors conducted a comprehensive analysis of a dataset provided by the State Grid Corporation of China, accessing authentic data from approximately 42,373 consumers. The comprehensive analysis of these extensive datasets lasted about 1035 days. For each consumer, an energy profile was created, summarizing their behavior over predefined periods of a day and a week. The authors encountered several challenges, including the absence of periodicity, which caused problems in the detection process, and the fact that fraud was difficult to detect. Another significant study was carried out by [PERSON] and colleagues in [34] to detect different forms of energy fraud by integrating AI algorithms, for performing different tasks such as load forecasting methods and the response that applications have when integrating them in the real world. The integrated algorithm emphasizes the aspects, and, for the anomaly detection component, data pertaining to cost, optimization, detection method, training, detection, and evaluation have been analyzed. The detection methods considered are based on regression and classification metrics combined with supervised learning modalities. The applicability of these methods in real-world settings is advantageous due to advanced computational strategies. However, they are also capable of addressing challenges such as complex and lengthy implementation processes, as well as updating energy systems and markets that lack advanced mechanisms for integrating measured data. However, the practical implementation of these methods may be constrained by factors such as power grid infrastructure limitations, concerns regarding consumer privacy, and environmental regulations. Energy theft has garnered significant interest among energy researchers, resulting in a growing number of studies on this topic. In [35], the negative aspects engendered by non-technical losses were analyzed. A novel algorithm was developed to identify fraudulent activities by employing a model known as Deep CNN, which utilizes genetic algorithms to analyze consumption data recorded by intelligent meters and evaluate potential impediments to energy mismanagement methods.