# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 4 Methods to Reduce Energy Losses
### Non-Technical Energy Losses
The methodology developed in the present work is based on fuzzy clustering to determine certain key positions for mounting smart meters, carried out based on the maximum likelihood criterion, so that the determined positions are the optimal ones from the point of view of minimizing losses. Furthermore, linear equations were implemented for the purpose of extracting load profiles for various customer categories. In conclusion, the approach adopted in this study demonstrates considerable promise in the identification of erroneous measurements, thereby contributing to a substantial reduction in non-technical losses. Energy researchers have made numerous contributions that have led to significant advancements in service quality in the field. Another impact research has been carried out by [PERSON], [PERSON] et al. in [77], where they have rigorously analyzed the importance of accurate measurements of consumption data, both at the consumer level and on the outgoing side to the distributors. Given the substantial volumes of data managed and stored by electricity supply entities, the integration of an expert system was anticipated to meticulously analyze all data, including energy consumption, technical specifications of measurement equipment, and other documentation derived from field inspections. The integrated expert system possesses the capacity to categorize and investigate customer data, identify potential discrepancies, and define the problem category. Furthermore, the integrated expert system possesses the capability to perform text mining and precise extraction of significant data. Utilizing these data, the system can develop rules for analysis and mining. The efficacy of this integrated approach is evidenced by a notable enhancement in the ratio of non-technical energy losses attributable to inaccurate measurements. Another study [78] underscores the significance of ascertaining non-technical energy losses to enhance the stability of power grids. Given the diversity of consumer profiles, there is the potential for a wide range of fraudulent activities that could affect the stability of the electricity supply. Consequently, the objective of this study is to propose a semi-supervised measurement method that analyzes data sets collected from the field to identify and mitigate fraud caused by inaccurate measurements. The researchers obtained results indicating enhanced measurement performance after the implementation of the intelligent model. The application of this calculation method has led to the development of a method of supervised analysis of the labeled data sets in order to be able to identify energy fraud suspects and also to analyze the unlabeled data represented by data in which the fraudulent activities are not integrated. The outcomes of the calculations indicate that the labeling process is more consistent than the classical manual methods, thus significantly shortening the process. In future research directions, the analysis of larger datasets is proposed to re-evaluate the energetic behavior of fraudulent customers. Non-technical losses are those that are not quantifiable or estimable; consequently, their ascertainment is a multifaceted process. In the study conducted by researchers [79], the problems that this branch of loss causes are addressed, and a method to stop this process, either fraud or measurement errors, is proposed. The analysis emphasizes the enhancement of efficiency through the replacement of conventional meters with advanced smart meters. While this approach has been adopted by other researchers, the present study incorporates a combination of traditional metering methods and the implementation of new ones. Additionally, field meter inspection methods were incorporated to streamline the process. The non-technical loss detection system that is the subject of this paper uses voltage sensitivity as well as power system optimization based on support vector machines.