# 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
**Refs.** & **Year** & **Success Rate [\%]** & **AI** & **Main Contribution** & **Methodology** & **Challenges** \\ \hline
[23] & 2021 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Hot-spot temperature & Thermal analysis & - \\ \hline
[24] & 2012 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Energy loss reduction & Utility data analysis & Data collection and analysis \\ \hline
[25] & 2006 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Power loss evaluation & Average loss calculation & Accurate loss determination \\ \hline
[26] & 2013 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Technical loss-estimation & Energy flow models & - \\ \hline
[27] & 2013 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Loss estimation method & Limited data analysis & - \\ \hline
[28] & 2001 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Cost calculation methodology & Life cycle cost analysis & - \\ \hline
[29] & 2022 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Overview of non-technical losses & - & Electricity theft acceptance \\ \hline
[30] & 2021 & \(\mathrm{N/A}\) & \(\mathrm{\check{\check{\check{\check{\check{\chi}}}}}}\) & Classifiers for non-technical loss identification & Performance comparison of classifiers & Handling imbalanced datasets \\ \hline
[31] & 2018 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Loss detection and localization & Smart grid-based detection & - \\ \hline
[32] & 2019 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Non-technical losses using OPF & Use of OPE classifier & - \\ \hline
[33] & 2023 & \(\mathrm{N/A}\) & \(\mathrm{\check{\check{\check{\check{\check{\
u}}}}}}\) & Fraud detection methods & Supervised/unsupervised learning & Privacy, adversarial attacks \\ \hline
[34] & 2024 & \(\mathrm{N/A}\) & \(\mathrm{\check{\check{\check{\check{\
u}}}}}\) & AI for energy optimization & Machine learning models & Model selection and accuracy \\ \hline
[55] & 2023 & & \(\mathrm{\check{\check{\check{\
u}}}}\) & Theft detection using smart meters & Deep learning for anomaly detection & Power usage anomalies \\ \hline
[36] & 2025 & \(\mathrm{N/A}\) & \(\mathrm{\check{\check{\check{\
u}}}}\) & IoT technologies & Free energy market and Energy Management System & False positives rate \\ \hline
[37] & 2023 & 86.667 & \(\mathrm{\check{\check{\check{\
u}}}}\) & Decision tree, support vector machine, Bayesian rule, neural network, and k-nearest neighbor classification & Precision, Recall, and F-Measure criteria & Accuracy of classifications \\ \hline
[38] & 2016 & 89 & \(\mathrm{\kappa}\) & State estimation theory and the recursive algorithm & Detection of bad measurement & Effect of temperature on the resistive impedance of lines \\ \hline
[39] & 2018 & 70 & \(\mathrm{\check{\check{\check{\
u}}}}\) & Support Vector Machines, Regression Tree & SM data and auxiliary databases & Non-malicious factors \\ \hline
[40] & 2020 & 94 & \(\mathrm{\check{\check{\check{\
u}}}}\) & Semi-Supervised Learning & Support vector machine, logistic regression, k-nearest neighbor, and random forest & The labor-consuming on-field inspection \\ \hline
[41] & 2012 & 82 & \(\mathrm{\kappa}\) & Residual Vector Update & The measurement set and normalized residual test & - \\ \hline
[42] & \multicolumn{4}{c}{94.48 (ANN)} & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\
[42] & 2012 & \(\mathrm{\check{\check{\check{\
u}}}}\)(SVM) & \(\mathrm{\check{\check{\check{\
u}}}}\) & WEXA software, ANN, SVM, k-NN & Implementations in C (programming language) and MATLAB & Selecting a subset of features \\  & \multicolumn{4}{c}{4.48} \\ \cline{2-6} \multirow{2}{*}{[43]} & \multirow{2}{*}{2015} & \multirow{2}{*}{72.43} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Supervised learning, OPF} & Classifiers the consumers in suspects and non-suspects using the OPF & Optimized meter allocation \\ \cline{5-5}  & & & & \\ \hline
[45] & 2018 & - & \(\mathrm{\kappa}\) & MATLAB simulations & State estimation and geometrically based & - \\ \hline
[47] & 2022 & 68.45 & \(\mathrm{\check{\check{\
u}}}\) & ANN multilayer perceptron & NTL detection framework based on endogenous and exogenous data & Difficulty in identifying consumption and temperature patterns \\ \hline
[48] & 2021 & - & \(\mathrm{\check{\check{\
u}}}\) & Random Under-Sampling Boosting (RUSBoost) technique and Long Short-Term Memory (LSTM) & Detection of billing errors, faulty meters, & \\ \hline
[49] & 2021 & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & (RUSBoost) technique and Long & electricity test using interpolation and non-smoking \\ \hline
[49] & 2021 & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & (RUS) normalization with CNN & Data imbalance \\ \hline
[49] & 2021 & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & (RUS) normalization with CNN & Handling the Missing Values \\ \hline
[50] & 2016 & 90 & \(\mathrm{\check{\check{\chi}}}\) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[51] & 2021 & \(\mathrm{N/A}\) & \(\mathrm{\check{\check{\chi}}}\) & Convolution neural networks, Fraud & Recall time series associated with uncommon frauds and time series & The generated ranked list is never \\ \hline
[53] & 2002 & - & \(\mathrm{\check{\chi}}\) & Binary integer programming with a branch and bound technique & The short-term optimal & - \\ \hline
[54] & 2016 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & Network reconfiguration & Knowledge-based heuristic methods & Radial network structure must \\  & \multicolumn{4}{c}{} & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & (RUS) nonlinear the distribution network & be maintained \\ \hline
[55] & 2021 & - & \(\mathrm{\kappa}\) & Stochastic fractal search algorithm & Optimal distributed generation & Operational constraints of \\ \hline
[56] & 1993 & \(\mathrm{N/A}\) & \(\mathrm{\check{\check{\chi}}}\) & \(\mathrm{\check{\check{\chi}}}\) & ANNs in FORTRAN language & Reconfiguration of the feeders in distribution systems & The training time of artificial \\ \hline
[57] & 2008 & - & \(\mathrm{\kappa}\) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[58] & 2002 & \(\mathrm{N/A}\) & \(\mathrm{\kappa}\) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline \end{tabular}
\end{table}