# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 5 Challenges and Limitations of the Currently Available Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Refs.** & **Year** & **Success Rate [\%]** & **AI** & **Main Contribution** & **Methodology** & **Challenges** \\ \hline
[60] & 2018 & N/A & ✓ & Particle swarm optimization & Optimally locate and size the Solid-State & - \\  & & & & Transformer installations & - \\ \hline
[61] & 2020 & 100 & ✓ & \
\begin{tabular}{c} MCSA and MOPSO, Long-term \\ planning, Short-term planning \\ \end{tabular} &
\begin{tabular}{c} Transformer allocation in distribution \\ network planning with MOSCA \\ and MOPSO. \\ \end{tabular} & Transformer allocation problem \\ \hline
[63] & 2005 & N/A & \(\times\) & Special active power line conditioner & Power converter of APLC to control & - \\  & & & & power flow to reduce losses of & - \\  & & & & distribution system & - \\ \hline
[62] & 2010 & N/A & ✓ & \
\begin{tabular}{c} Particle swarm optimization, \\ quasi-Newton method, genetic based \\ \end{tabular} &
\begin{tabular}{c} Power transmission loss with \\ search methods \\ \end{tabular} & Near global solution \\ \hline
[65] & 2006 & - & \(\times\) & Continuous Switch Modeling & Distribution system reconfiguration & - \\  & & & based on optimum power flow & \\ \hline
[66] & 2008 & N/A & ✓ & \
\begin{tabular}{c} Joint optimization algorithm, \\ adaptive genetic algorithm (AGA) \\ \end{tabular} &
\begin{tabular}{c} Network reconfiguration and \\ capacitor control \\ \end{tabular} & Not all reconfiguration switches can & \\  & & & & capacitor control & be remotely controlled \\ \hline
[67] & 2012 & N/A & \(\times\) & Conventional maximum &
\begin{tabular}{c} Multifunctional complex controller \\ compensates for the nonlinear \\ distortion and reduces the voltage of \\ the THD. \\ \end{tabular} & - \\ \hline
[68] & 2020 & 77.96 & ✓ & \
\begin{tabular}{c} Adaptive particles swarm \\ optimization (APSO), \\ backward/forward sweep method \\ \end{tabular} &
\begin{tabular}{c} The APSO algorithm determines the \\ required amount of reactive and/or \\ active power to minimize the total losses \\ \end{tabular} & Time varying constants \\ \hline
[69] & 2018 & 14.75 & ✓ &
\begin{tabular}{c} Classification and Regression tree \\ and a Self-Organizing Map \\ neural network \\ \end{tabular} & Methodology to increase the inspections & - \\ \hline
[70] & 2006 & 58 & \(\times\) & \
\begin{tabular}{c} Installation of burglar alarm systems, \\ remote connection and reconnection \\ systems, conductor protections to \\ reduce fraud, imprecision of the \\ measurement, reading errors or bad \\ management of the data \\ collection systems \\ \end{tabular} &
\begin{tabular}{c} Performance of the \\ administrative part \\ management of the data \\ collection systems \\ \end{tabular} \\ \hline
[71] & 2020 & 90 & \(\times\) & Smart metering optimization & Systematic review & Data accuracy, privacy \\ \hline
[72] & 2014 & 82 & \(\times\) & \
\begin{tabular}{c} Evaluated distribution \\ network performance \\ \end{tabular} &
\begin{tabular}{c} Implemented \\ energy-efficient equipment \\ \end{tabular} & Technical and non-technical losses \\ \hline
[73] & 2014 & N/A & \(\times\) & Monitoring non-technical losses & Designed monitoring equipment & Detecting illegal connections \\ \hline
[74] & 2014 & 66.4 & ✓ & Enhanced NTL detection & Statistical analysis, text mining & Identifying NTLs \\ \hline
[75] & 2016 & N/A & \(\times\) & Voltage drop measurement & Point-to-point voltage monitoring & Non-technical loss detection \\ \hline
[76] & 2020 & N/A & \(\times\) & NTL detection using analytics & Bad data analysis & Illegal connections detection \\ \hline
[77] & 2011 & N/A & ✓ &
\begin{tabular}{c} NTLanalysis system \\ \end{tabular} & Data mining, rule-based reasoning & Data processing complexity \\ \hline
[78] & 2014 & N/A & ✓ & \begin{tabular}{c} Developed semi-supervised method \\ for NTL detection \\ \end{tabular} & \
\begin{tabular}{c} Semi-supervised learning on \\ consumption data \\ \end{tabular} &
\begin{tabular}{c} Imbalanced datasets and \\ fraud detection \\ \end{tabular} \\ \hline
[79] & 2019 & 100 & ✓ & Hybrid method for NTL detection & \
\begin{tabular}{c} SVM, time series analysis, \\ feature extraction \\ \end{tabular} &
\begin{tabular}{c} Fraud detection in smart grids, \\ voltage sensitivity \\ \end{tabular} \\ \hline
[80] & 2020 & N/A & \(\times\) & Algorithm for loss detection & Smart metering, loss correction & Distinguishing technical from \\  & & & & nontechnical losses \\ \hline
[81] & 2020 & N/A & ✓ & Improved NTL detection accuracy & Machine learning, data segmentation & Ensuring explainability in results \\ \hline
[82] & 2021 & N/A & \(\times\) & \
\begin{tabular}{c} Regulatory evaluation approach \\ for NTLs \\ \end{tabular} &
\begin{tabular}{c} Kriging method, inspection datasets \\ \end{tabular} & Regulatory planning \\ \hline
[83] & 2008 & 50 & ✓ & Electricity theft detection & SVM on consumption data & Identifying fraudulent consumption \\ \hline
[84] & 2023 & 89.4 & ✓ & \
\begin{tabular}{c} Detection of non-technical losses in \\ irritant consumers \\ \end{tabular} &
\begin{tabular}{c} AI-based detection techniques applied \\ to irrigation data \\ \end{tabular} & - \\ \hline
[85] & 2020 & 98\% & ✓ & \
\begin{tabular}{c} Boosted classification approach for \\ NTL detection \\ \end{tabular} &
\begin{tabular}{c} Boosted CS,0 decision tree \\ for classification \\ \end{tabular} & Handling imbalanced data \\ \hline
[86] & 2026 & N/A & ✓ & Identifying critical weak points & Network theory applied to \\  & & & & energy systems & Modeling interconnected networks \\ \hline
[87] & 2023 & N/A & ✓ & Fraud detection in finance, energy & AI for anomaly detection & Ensuring accuracy and data security \\ \hline
[88] & 2013 & N/A & \(\times\) & Reducing utility billing errors & System dynamics approach & Addressing billing inaccuracies \\ \hline
[89] & 2019 & 77 & ✓ & Reducing billing costs & Seasonality removal, Empirical Bayes & Addressing seasonal fluctuations \\ \hline
[90] & 2016 & N/A & \(\times\) &
\begin{tabular}{c} Meter tampering causes NTL \\ \end{tabular} & Analysis of tampering incidents & Reducing meter tampering \\ \hline
[91] & 2017 & N/A & ✓ &
\begin{tabular}{c} Detects energy theft, faulty \\ smart meters \\ \end{tabular} & Linear regression-based algorithms & Linear regression-based algorithms \\ \hline
[92] & 2024 & N/A & \(\times\) & Analyzing non-technical losses & Global analysis of losses & Reducing financial impact \\ \hline
[93] & 2018 & 94.15 & \(\times\) & Wireless automated energy metering & RF-based wireless system & Reducing non-technical losses \\ \hline \hline \end{tabular}
\end{table}