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:  I. INTRODUCTION 
1. Abstract of Cited Paper (BIB001):  There have been a number of reforms in the power sector in Bangladesh since her independence, but these reforms failed to bring desired improvements in the power sector. Among the three main components of the power system, recent reform activities were centered around generation and transmission. The most pressing problem in the power sector has been with the distribution system, which is characterized by heavy system loss and poor collection performance; however, the distribution system seldom got the priority in reform initiatives. Thus, it becomes very difficult to derive benefits from the reforms while leaving the distribution system untouched. This implies that priority in reform must be given to the distribution. To make it efficient and effective, its administration must be restructured. At the same time, its performance should be monitored continually on the basis of particular performance indicators. It is found that system loss in any power distribution system in Bangladesh should not exceed 10%, i.e. collection–import (CI) ratio should be kept above 90%. To maintain the system loss and the CI ratio at the recommended level, the malpractice by the utility personnel must be stopped fast. This will help to achieve economic sustainability. 
2. Abstract of Cited Paper (BIB002):  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%. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  We study incentive problems in electricity distribution when customer energy usage is imperfectly observable by the utility. Thus, we assume that each customer has private information about the amount of his consumed energy. Imperfect observability of individual user demand results is non-technical energy losses. In developing countries, these losses amount to 20 − 30% per year, and are largely attributed to theft by residential customers. Reducing these losses will allow a marked increase in efficiency of the electricity distribution. Usage of smart energy management devices enables new functionalities and brings the potential for such increased efficiency. However, employing smart energy management devices also entails a new set of problems. Typically, such devices are commercially produced, and employ off-the-shelf information technology (IT) solutions with inherent security vulnerabilities. Thus, due to technology limitations and cost constraints, smart devices are vulnerable to tampering and may enable systemic energy theft, threatening to reduce, or even erase the gains in efficiency. In this paper, we address incentives of utility company to combat theft (i.e., non-technical losses), when utility is subject to rate (tariff) regulation. From our analysis, such regulated utilities invest less than socially optimal in theft reduction. We suggest that regulators should include explicit targets for the allowable losses to remedy the problem of incentive misalignment. 
5. Abstract of Cited Paper (BIB005):  Utilities experience significant revenue fatalities due to Non-Technical Losses (NTLs). These losses are principally caused by fraud activities deliberately performed by the consumers. NTLs lead to a series of additional losses, including damage to grid infrastructure and reduction of grid reliability. This paper presents arigorous review of technologies, methodologies and active approaches aimed at improving with emphasis in the accurate estimation and reduction of NTLs. Additionally, the sources of NTLs along with the possible impacts of NTLs on the economy is also focused. 
6. Abstract of Cited Paper (BIB006):  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:
O UR modern society and daily activities strongly depend on the availability of electricity. Electrical power grids allow to distribute and deliver electricity from generation infrastructure such as power plants or solar cells to customers such as residences or factories. One frequently appearing problem are losses in power grids, which can be classified into two categories: technical and non-technical losses. Technical losses occur mostly due to power dissipation. This is naturally caused by internal electrical resistance and the affected components include generators, transformers and transmission lines. The opposite class of losses are non-technical losses (NTL), which are primarily caused by electricity theft. In most countries, NTLs account for the predominant part of the overall losses BIB003 . Therefore, it is most beneficial to first reduce NTLs before reducing technical losses BIB005 . Nonetheless, reducing technical losses is challenging, too. In particular, NTLs include, but are not limited to, the following causes BIB006 , BIB001 Manuscript received MONTH DD, YEAR. • Meter tampering in order to record lower consumptions • Bypassing meters by rigging lines from the power source • Arranged false meter readings by bribing meter readers • Faulty or broken meters • Un-metered supply • Technical and human errors in meter readings, data processing and billing NTLs cause significant harm to economies, including loss of revenue and profit of electricity providers, decrease of the stability and reliability of electrical power grids and extra use of limited natural resources which in turn increases pollution. There are different estimates of the losses caused by NTL. For example, in India, NTLs are estimated at US$ 4.5 billion  . NTLs also reported to range up to 40% of the total electricity distributed in countries such as Brazil, India, Malaysia or Lebanon BIB007 , BIB004 . They are also of relevance in developed countries, for example estimates of NTLs in the UK and US range from US$ 1-6 billion BIB002 , BIB003 . In order to detect NTLs, inspections of customers are carried out based on predictions. From an electrical engineering perspective, one method to detect losses is to calculate the energy balance  , which requires topological information of the network. This does not work accurately for those reasons: (i) network topology undergoes continuous changes in order to satisfy the rapidly growing demand of electricity, (ii) infrastructure may break and lead to wrong energy balance calculations and (iii) it requires transformers, feeders and connected meters to be read at the same time. A more flexible and adaptable approach is to employ artificial intelligence (AI)  . AI allows to analyze customer profiles, their data and known irregular behavior in order to trigger a possible inspection of a customer. However, carrying out inspections is costly, as it requires physical presence of technicians. It is therefore important to make accurate predictions in order to reduce the number of false positives. The rest of this paper is organized as follows. Section II describes the field of AI. Section III provides a detailed review and critique of state-of-the-art NTL detection research employing AI methods. In Section IV, we identify the key challenges of this field that need to be accurately studied in order to enhance methods in the future. To the best of our knowledge, this topic has not been addressed yet in the literature on NTL detection. Section V summarizes this survey.