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:  C. Expert systems and fuzzy systems 
1. Abstract of Cited Paper (BIB001):  The volume of energy loss that Brazilian electric utilities have to deal with has been ever increasing. The electricity concessionaries are suffering significant and increasing loss in the last years, due to theft, measurement errors and many other kinds of irregularities. Therefore, there is a great concern from those companies to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents the proposal of an intelligent system, composed of two neural networks ensembles, which intends to increase the level of accuracy in the identification of irregularities among low tension consumers. The data used to test the proposed system are from Light S.A. Company, the Rio de Janeiro concessionary. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company. 
2. Abstract of Cited Paper (BIB002):  The volume of energy loss that Brazilian electrical utilities have to deal with has been ever increasing. Electricity distribution companies have suffered significant and increasing losses in the last years, due to theft, measurement errors and other irregularities. Therefore there is a great concern to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents a combined approach of a neural networks committee and a neuro-fuzzy hierarchical system intended to increase the level of accuracy in the identification of irregularities among low voltage consumers. The data used to test the proposed system are from Light S.A., the distribution company of Rio de Janeiro. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company. Keywords— neural nets, hierarchical neuro-fuzzy systems, binary space partition, electricity distribution, fraud detection. 
3. Abstract of Cited Paper (BIB003):  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 an approach towards detection of Non-technical Losses (NTLs) of Large Power Consumers (LPC) in Tenaga Nasional Berhad (TNB) Malaysia. The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in Malaysia to reduce its NTLs in the LPC distribution sector. Remote meters installed at premises of LPC customers transmit power consumption data including remote meter events wirelessly to TNB Metering Services Sdn. Bhd. The remote meter reading (RMR) consumption data for TNB LPC customers is recorded based on half-hourly intervals. The technique proposed in this paper correlates the half-hourly RMR consumption data with abnormal meter events. The correlated data provides information regarding consumption characteristics i.e. load profiles of LPC customers, which helps to expose abnormal consumption behavior that is known to be highly correlated with NTL activities and electricity theft. Pilot testing results obtained from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection of LPC customers in peninsular Malaysia indicate the proposed NTL detection technique is effective with a 55% detection hitrate. With the implementation of this intelligent system, NTL activities of LPC customers in TNB Malaysia will reduce significantly. 
4. Abstract of Cited Paper (BIB004):  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%. 
5. Abstract of Cited Paper (BIB005):  This letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy IF-THEN rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distribution's detection hitrate has increased from 60% to 72%, thus proving to be cost effective. 
6. Abstract of Cited Paper (BIB006):  Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.

Literature Review:
Profiles of 80K low-voltage and 6K high-voltage customers in Malaysia having meter readings every 30 minutes over a period of 30 days are used in BIB004 for electricity theft and abnormality detection. A test recall of 0.55 is reported. This work is related to features of BIB006 , however, it uses entirely fuzzy logic incorporating human expert knowledge for detection. A database of~700K Brazilian customers,~31M monthly meter readings from January 2011 to January 2015 and~400K inspection data is used in BIB007 . It employs an industrial Boolean expert system, its fuzzified extension and optimizes the fuzzy system parameters using stochastic gradient descent  to that database. This fuzzy system outperforms the Boolean system. Inspired by BIB001 , a SVM using daily average consumption features of the last 12 months performs better than the expert systems, too. The three algorithms are compared to each other on samples of varying fraud proportion containing~100K customers. It uses the area under the (receiver operating characteristic) curve (AUC), which is discussed in Chapter IV-A. For a NTL proportion of 5%, it reports AUC test scores of 0.465, 0.55 and 0.55 for the Boolean system, optimized fuzzy system and SVM, respectively. For a NTL proportion of 20%, it reports AUC test scores of 0.475, 0.545 and 0.55 for the Boolean system, optimized fuzzy system and SVM, respectively. Five features of customers' consumption of the previous six months are derived in  : average consumption, maximum consumption, standard deviation, number of inspections and the average consumption of the residential area. These features are then used in a fuzzy c-means clustering algorithm to group the customers into c classes. Subsequently, the fuzzy membership values are used to classify customers into NTL and non-NTL using the Euclidean distance measure. On the test set, an average precision (called average assertiveness) of 0.745 is reported. The database of BIB002 is used in BIB003 . In the first step, an ensemble pre-filters the customers to select irregular and regular customers for training which represent well two different classes. This is done because of noise in the inspection labels. In the classification step, a neuro-fuzzy hierarchical system is used. In this setting, a neural network is used to optimize the fuzzy membership parameters, which is a different approach to the stochastic gradient descent method used in BIB007 . A precision of 0.512 and an accuracy of 0.682 on the test set are obtained. The work in BIB005 is combined with a fuzzy logic expert system postprocessing the output of the SVM in BIB006 for 100K customers. The motivation of that work is to integrate human expert knowledge into the decision making process in order to identify fraudulent behavior. A test recall of 0.72 is reported.