# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses
#### 3.2.3 Energy Losses Caused by Poor Infrastructure and Resource Management  
Another approach to identifying non-technical energy losses was developed by the authors [PERSON] et al. [41]. In their study, the researchers implemented a method for determining losses based on smart meters located at the consumption site. Giventheir capacity to amass substantial data, these meters were able to interpret the relevant information, thereby facilitating the identification of non-technical losses. The study's primary objective is the detection and identification of illegal load connections within electricity networks. To this end, a comprehensive analysis of 34 low-voltage bus lines and 1682 medium-voltage bus lines was conducted, with the objective of identifying and extracting problems caused by illegal activities within the electrical domain. In the paper [42], the authors discussed an intelligent computational technique for identifying non-technical losses based on an intelligent tool analyzing different consumption profiles. To fulfill the objective of the paper, WEKA software 3.6 was used to compare and utilize specific classification classes to be implemented in AI algorithms. In a similar vein, researchers have developed a method for identifying these losses by analyzing consumer types [43]. Addressing the efficiency challenge, they employed Optimum Path Forest for the classification of certain consumer types. The proposed method aims to identify fraud and irregularities in power networks. The efficacy of the proposed method was assessed by conducting a trial in the IEEE 123 Bus Test Feeder test environment, which yielded a success rate of 72.43% for consumer inspections. Another approach to identifying non-technical losses in power grids was also realized by the authors in [44] through the integration of software for detecting losses of this type. To develop the prototype, datasets were integrated and analyzed by software engineering, thereby enabling the implementation of a technical solution. This solution is the culmination of a comprehensive and exhaustive analysis of all parameters and variables involved in the process. The subject of losses in the energy field is of significant interest to researchers, and [PERSON], [PERSON], and [PERSON] addressed the development of a tool for determining non-technical losses in their work [45]. This tool assists distribution operators in locating and quantifying these losses. The researchers developed a methodology based on geometric estimation of energy losses and localization of their source. After the geographical and condition estimation of the networks and losses, hypotheses are designed by the researchers. These hypotheses allow the distribution operator to analyze the affected nodes. The operator can verify the hypotheses by approving or rejecting them. To validate the efficacy of the proposed methodology, a comprehensive experimental study was conducted on two IEEE feeder networks. In the context of non-technical energy loss analysis, the implementation of load curves for consumers emerges as a key element. In [46], researchers have addressed the realization of profiles for different types of consumers, and, based on their analysis, possible anomalies can be identified. The central problem that this study focuses on is the development of a methodology that incorporates performance metrics to facilitate the analysis of consumer profiles. The evaluation method is structured in a manner that enables the analysis and preparation of the datasets to accommodate the potential for missing values in the acquired data. The proposed identification method involves analyzing data sequences to derive a statistic that quantifies the behavior of each consumer profile created. In the research [47], the authors conducted a thorough analysis of the issues associated with non-technical losses, with a primary focus on the investigation of anomalous data that has emerged throughout the course of the data acquisition from the field.