## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Problem Addressed** & **Solution Proposed** & **Dataset** & **Limitations** \\ \hline One-dimensional data fails to capture periodicity [14] & Wide and deep CNN & SGCC (2014-2016) & Computational complexity, FPR is not calculated, Imbalance class problem \\ \hline Required label dataset with additional information, poor accuracy [22] & Combine two techniques. MIC-CFSFDP & Irish CER smart metering project & Additional hardware cost FPR is not calculated, Low detection accuracy \\ \hline NTL detection [19] & RusBoost and MODWPT & Industrial and commercial sectors in Honduras of China & Underfitting, time complexity Selection of a biased sample, loss of information \\ \hline Feature engineering required [21] & BBHA & Brazilian electricity regulatory agency & Trapped in local minima, No preprocessing, Imbalance class problem \\ \hline Hacking of smart meters [17] & Combine two techniques. K-means and DNN & Irish (2012) & Imbalance dataset \\ \hline Smart meter hacking, counterfeit the data [18] & Finite mixture model clustering and a genetic programming algorithm & Real time data from Ireland in (2009–2010) & High FPR \\ \hline NTL detection and accuracy is low [11] & Proposed a methodology in which collected data is represented as image with the help of deep learning. & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} \\ \hline Electricity theft detection and high FPR [24] & LSTM & Electricity load diagrams (2011–2014) & High prediction error, High delay time in anomaly occur and detection \\ \hline Loss between electricity usage and electricity supply [25] & Fuzzy logic & Customers monthly invoiced bills & Require experts for analyses, Complex, Takes a lot of time to develop Fuzzy rules \\ \hline Less number of verified customers in dataset, due to which accuracy is compromised [7] & GBTD & Smart energy dataset by Irish & Computationally expensive \\ \hline ETD in IoT-based network [26] & SETs & Data is collected from Aeon lab named as Z-Wave based in UK & Additional hardware cost and maintenance cost \\ \hline NTL detection [27] & Multiple Linear Regression model & Ministry of Power distribution based in India (2013) & Not explain the impact of accurately detected users \\ \hline Detection of energy theft in utilities is a challenge [28] & Implement machine learning technique in utility company to detect gap between generation and consumption & Data is collected from leading electricity provider in Spain & FPR is not calculated, Imbalance dataset \\ \hline NTL detection is performed on synthetic data set to achieve low FPR [29] & GK clustering & Data is collected from commission for energy regulation based in Irish & Class imbalance problem, Need large data and experts, Time complexity is high \\ \hline NTL detection in Pakistan [30] &
\begin{tabular}{} \end{tabular} & MEPCO in Pakistan & Computationally expensive \\ \hline NTL detection [31] & PNN and Levenberg–Marquardt & Pennesylvania–New Jersey–Maryland (PJM) & Accuracy is low, FPR is not Laxed, Largest power system calculated, imbalance dataset \\ \hline Not rank the customers according to their anomalous behavior [32] & XG Boost & Data is collected from commercial and industrial users of Endesa & Cannot handle large data, High execution time, memory hungry \\ \hline NTL detection [33] & CNN and RF & Data is collected from sustainable energy authority of Ireland and electricity utility of London & Time complexity is high because of hybrid model \\ \hline Energy theft [34] & Combines CNN and LSTM for theft detection & SGCC & High execution time \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of existing methodologies. The authors have deployed a deep learning methodology in which data is represented as an image [11]. This methodology is specifically designed to accommodate large scale data.