# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 4 Methods to Reduce Energy Losses
### Non-Technical Energy Losses
The system's capacity to discern outage data from consumption time series, derived from the training databases, is a notable feature. The system is also equipped with an integrated voltage analysis component that assesses sensitivity. The return of concrete values was achieved through the optimization of the semi-definite programming process using a related nonlinear technique. In conclusion, the system has been tested in different configurations and scenarios to demonstrate its effectiveness. Another impactful approach was also made by [PERSON], [PERSON], and [PERSON] in [80], where they addressed the importance of accurately detecting the types of existing non-technical type losses. Consequently, their study is predicated on the precise determination of technical losses, thereby enabling accurate assessment of non-technical losses. To this end, they developed an algorithm that considers the functions of remote characterization and control of cable impedances, as well as the classification of both technical and non-technical losses. The integration of a smart meter has been conceptualized to discern any meters in the field that are not metering or are mis-metering energy, thereby attracting non-technical losses. The proposed algorithm utilizes a modified version of the Hamming code, whereby it can determine losses caused by distribution cable accidents and estimate active and reactive losses, illegal splicing in power grids, and even frauds through cyber-attacks. The outcomes of this study substantiate the enhanced efficiency and precision that is a consequence of the integration of the proposed algorithm. In the domain of energy, the imperative to curtail energy losses has emerged as a pivotal concern that has garnered the attention of researchers and national competent authorities alike. In [81], the authors presented the problems faced by electricity grids from the perspective of energy losses, emphasizing fraud and billing and metering errors, both of which fall into the category of non-technical losses. The presented study offers a comprehensive treatment of the existential problem of non-technical losses, which have persisted to the present stage. The detection and reduction of this loss category are achievedby ML solutions designed to optimize the loss identification process. The integration of ML algorithms into energy companies' databases enables the automation of decision-making processes, facilitating the identification and mitigation of non-technical losses. The outcomes derived from the implementation of this methodology are encouraging, and the reduction in losses has yielded numerous advantages within the industrial sector. The utilization of these algorithms in future applications would be greatly enhanced by the improvement of data study extensions, leading to optimal outcomes. A notable limitation identified by this study is that human experts do not understand the machine code language, which makes the data interpretation process difficult. Therefore, methods of integrating natural language and causal graphs to exemplify system characteristics are considered for further development. In regions where electricity grids are susceptible to fraudulent activities, rigorous analyses are conducted to identify and address these threats. For instance, in the paper [82], a bus in Brazil was analyzed to reduce energy fraud in distribution systems. The authors of this study implemented computational software based on regulatory analysis of losses by inspecting the datasets recorded by energy suppliers' meters. The Kriging method was then employed to estimate geographical positions and precisely localize areas with significant utility losses. The implementation of this method led to the identification and subsequent elimination of fraud in the analyzed electricity grids. In [83], the authors [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] addressed the issue of electricity theft, which is facilitated by illegal reconnection.