You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey 
Chapter Title:  A. Support Vector Machines 
1. Abstract of Cited Paper (BIB001):  Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBD's detection hitrate will increase from 3% to 60%. 
2. Abstract of Cited Paper (BIB002):  Most utility companies in developing countries are subjected to major financial losses because of non-technical losses (NTL). It is very difficult to detect and control potential causes of NTL in developing countries due to the poor infrastructure. Electricity theft and billing irregularities form the main portion of NTL. These losses affect quality of supply, electrical load on the generating station and tariffs imposed on electricity consumed by genuine customers. In light of these issues, this paper discusses the problems underlying detection of electricity theft, previously implemented ways for reducing theft. In addition, it presents the approximate energy consumption patterns of several customers involving theft. Energy consumption patterns of customers are compared with and without the presence of theft. A dataset of customer energy consumption pattern is developed based on the historical data. Then, support vector machines (SVMs) are trained with the data collected from smart meters, that represents all possible forms of theft and are tested on several customers. This data is classified based on rules and the suspicious energy consumption profiles are grouped. The classification results of electricity consumption data are also presented. 
3. Abstract of Cited Paper (BIB003):  Transmission and Distribution (T&D) of electricity from a power generation station involve substantial losses. T&D losses include technical as well as nontechnical losses (NTL). Most portion of the NTL constitutes of electricity theft. This paper explains the significance of the evaluation of customer energy consumption profiles for identification of illegal consumers. To reduce the complexity of the instantaneous energy consumption data for evaluation, this paper proposes and implements a data encoding technique. This encoding technique maps instantaneous energy consumption data into irregularities in consumption. In addition, exclusivity in each customer's energy consumption has been preserved. After the encoding process, the data has been inputted to a support vector machine (SVM) classification model that classifies customers into three categories: genuine customers, illegal consumers or suspicious customers. Classification accuracy of the SVM model with the encoded data is 92%. The obtained results demonstrate that this encoding procedure is significantly quick and robust in identifying (classifying) illegal consumers. 
4. Abstract of Cited Paper (BIB004):  This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. 
5. Abstract of Cited Paper (BIB005):  Abstract Transmission and distribution of electricity involve technical as well as Non-Technical Losses (NTLs). Illegal consumption of electricity constitutes a major portion of the NTL at distribution feeder level. Considering the severity and devastating effects of the problem, illegal consumption of electricity has to be detected instantly in real-time. To this end, this paper investigates the possibility and role of High Performance Computing (HPC) algorithms in detection of illegal consumers. This paper designs and implements an encoding procedure to simplify and modify customer energy consumption data for quicker analysis without compromising the quality or uniqueness of the data. This paper parallelizes overall customer classification process. The parallelized algorithms have resulted in appreciable results as displayed in the results section of the paper.

Literature Review:
Support Vector Machines (SVM)  are a state-of-the-art classification algorithm that is less prone to overfitting. Electricity customer consumption data of less than 400 highly imbalanced out of~260K customers in Kuala Lumpur, Malaysia having each 25 monthly meter readings in the period from June 2006 to June 2008 are used in BIB001 . From these meter readings, daily average consumptions features per month are computed. Those features are then normalized and used for training in a SVM with a Gaussian kernel. For this setting, a recall of 0.53 is achieved on the test set. In addition, credit worthiness ranking (CWR) is used in BIB002 . It is computed from the electricity provider's billing system and reflects if a customer delays or avoids payments of bills. CWR ranges from 0 to 5 where 5 represents the maximum score. It was observed that CWR significantly contributes towards customers committing electricity theft. A test accuracy of 0.77 and a test recall of 0.64 are reported. SVMs are also applied on 1,350 Indian customer profiles in BIB003 . They are split into 135 different daily average consumption patterns, each having 10 customers. For each customer, meters are read every 15 minutes. A test accuracy of 0.984 is reported. This work is extended in BIB004 by encoding the 4×24 = 96-dimensional input in a lower dimension indicating possible irregularities. This encoding technique results in a simpler model that is faster to train while not losing the expressiveness of the data and results in a test accuracy of 0.92. This work is extended in BIB006 by introducing high performance computing algorithms in order to enhance the performance of the previously developed algorithms in BIB004 . This faster model has a test accuracy of 0.89. Consumption profiles of 5K Brazilian industrial customer profiles are analyzed in BIB005 . Each customer profile contains 10 features including the demand billed, maximum demand, installed power, etc. In this setting, a SVM slightly outperforms K-nearest neighbors (KNN) and a neural network, for which test accuracies of 0.9628, 0.9620 and 0.9448, respectively, are reported.