# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 4 Methods to Reduce Energy Losses
### Non-Technical Energy Losses
In their research, the authors approached the analysis of non-technical losses through an AI technique called Support Vector Machine (SVM). This intelligent software was deployed in the Malaysia area due to the high degree of energy theft in that area, and the main objective was to reduce or even eliminate it permanently. The proposed model aims to identify suspect customers based on their anomalous energy consumption patterns. The SVM method utilizes customer profile information to categorize deviations as non-technical losses resulting from theft. Following a thorough analysis of the available databases, a classification system is developed to identify potential suspects and subsequent field inspections are conducted. The simulation results indicate the efficacy of the proposed method, leading to a substantial reduction in activities related to non-technical losses. The method introduced in this paper can reliably detect fraudulent consumers by utilizing the concept of SVM hypersurface division. Therefore, after the implementation of the solution, a success rate of 50% was achieved, thus improving the economy by detecting illegal behavior. Non-technical losses have also been analyzed in [84], where the primary objective was to mitigate the financial losses incurred by this category of energy losses. These losses can be significantly reduced, in contrast with the technical ones. The study examined electricity grids in rural environments, where the prevalence of fraudulent customers is higher. The designed system, underpinned by AI algorithms, meticulously analyzes historical consumption data, meteorological data, and other pertinent information provided by distribution operators to identify fraudulent activities. The methodology is meticulously delineated in a \"pilot\" region, with each step being elucidated. The outcomes of this study are encouraging, as they demonstrate an 89% success rate in detecting anomalies and a 78% reduction in search areas. These results affirm the efficacy of the proposed methodology and lay the foundation for expanding the study to regions with higher energy consumption. Within the modern economy, AI algorithms have become indispensable tools in various industries. In the paper [85], a non-technical loss determination procedure based on several AI techniques was investigated to determine several types of irregularities and to analyze several decision factors. The selection of data is determined by Pearson'sequation, followed by a proposed decision tree algorithm to achieve a classification of consumers into two categories: fraudulent and honest. The efficacy of this detection method is then benchmarked against other ML methods, including Random Forest, SVM, ANN, and Extreme Gradient Boosting. A thorough analysis of the proposed method and the methods reveals that the proposed method achieves optimal results, with an accuracy of 94.6%, a sensitivity of 78.1%, and superiority over the other methods, as evidenced by a comprehensive comparative analysis. The identification of illegal activity is essential because fraud accounts for a significant amount of lost energy. Consequently, there is a strong focus on the accurate identification of abnormal sources and vulnerabilities in the power system. In [86], the authors conducted a detailed study in which they presented methods for detecting weak points in power grids and proposed methods for their reduction and prevention. The author's approach was to integrate an algorithm that examines the output power of the equipment and the network topology to analyze the network weaknesses. The integration of an algorithm for determining the shortest path of the power supply topology was implemented to enhance efficiency and minimize losses caused by these weak points. The evaluation indicators relate to vulnerability management in the case of system fragmentation and voltage losses. The study's findings were validated by integrating six distinct strategies aimed at eradicating the identified vulnerabilities present on lines and networks.