# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **Year** & **Success Rate [\%]** & **AI** & **Main Contribution** & **Methodology** & **Challenges** \\ \hline
[94] & 2008 & N/A & \(\times\) & Smart self-healing power grid & Infrastructure review & Reliability \\ \hline
[95] & 2020 & 50-95 & \(\times\) & Grid-connected renewable challenges & Review of technical issues & Integration and stability concerns \\ \hline
[96] & 2020 & N/A & \(\times\) & Anomaly detection framework & Cloud-based detection methods & Edge computing \\ \hline
[60] & 2018 & N/A & ✓ & Particle swarm optimization & Optimally locate and size the Solid-State & - \\  & & & & Transformer installations & - \\ \hline
[97] & 2022 & N/A & \(\times\) & Energy theft detection taxonomy & Comparative analysis & Limitations \\ \hline
[98] & 2018 & N/A & \(\times\) & Energy infrastructure and & & \\  & & & national impact & Editorial on energy issues & Sustainability \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._are frequently attributable to various factors, including social, economic, and managerial elements. These losses are more challenging to estimate and identify due to the intricacy of strategies for integrating intelligent methods and the intricacy of integrating processes to reduce these types of losses, either by monitoring billing processes, anti-fraud methods, or by monitoring consumption scenarios. A significant component of the investigation of energy losses is the literature review, which has highlighted innovative solutions for identifying energy losses. These solutions have been proposed by several researchers and include the integration of AI and big data analytics, the implementation of smart metering to combat fraud and incomplete billing, and the promotion of transparency in the process of energy supply to end-users. Consequently, these researchers' approaches address the root causes of losses and, furthermore, contribute to enhancing the stability of energy systems by proposing methods to combat the adverse effects engendered by these losses. In conclusion, the proposed methods for reducing technical and non-technical losses necessitate a multidimensional approach, integrating both technical solutions and field measurements, which are analyzed in accordance with current energy regulations. The significance of studies in this field is twofold: first, they contribute to enhanced energy efficiency, and, second, they offer economic benefits, thereby aligning with the overarching objectives of enhancing sustainability. Therefore, this study underscores the necessity for sustained research in the domain of energy losses that is meticulously tailored to the context of each distinct electricity network under scrutiny. Conceptualization, D.F.N. and R.T.; methodology, R.T.; validation, D.F.N., R.T. and S.P. ; formal analysis, H.B. ; investigation, D.F.N. and A.C; resources, D.F.N. and M.M. ; data curation, R.T. and S.P. ; writing--original draft preparation, D.F.N. ; writing--review and editing, D.F.N., H.B. and M.M; visualization, C.A. ; supervision, R.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The datasets presented in this paper are readily available on Scopus, Web of Science, and Google Scholar. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
AI Artificial Intelligence  
TOC Technology Own Consumption  
PT Transformer substation  
TS Transformer Station  
ANN Artificial Neural Network  
LTSM Long Short-Term Memory  
RUSBoot Random Under-Sampling Boosting  
ML Machine Learning  
SVM Support Vector Machine  
LCD Liquid Crystal Display  
SCADA Supervisory Control and Data Acquisition