The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> This paper describes a generalization of the inverse of a non-singular matrix, as the unique solution of a certain set of equations. This generalized inverse exists for any (possibly rectangular) matrix whatsoever with complex elements. It is used here for solving linear matrix equations, and among other applications for finding an expression for the principal idempotent elements of a matrix. Also a new type of spectral decomposition is given. <s> BIB001 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> 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 Non-Technical Loss (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities. <s> BIB002 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> 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. <s> BIB003 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time. <s> BIB004 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail. <s> BIB005 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> Nowadays the electric utilities have to handle problems with the non-technical losses caused by frauds and thefts committed by some of their consumers. In order to minimize this, some methodologies have been created to perform the detection of consumers that might be fraudsters. In this context, the use of classification techniques can improve the hit rate of the fraud detection and increase the financial income. This paper proposes the use of the knowledge-discovery in databases process based on artificial neural networks applied to the classifying process of consumers to be inspected. An experiment performed in a Brazilian electric power distribution company indicated an improvement of over 50% of the proposed approach if compared to the previous methods used by that company. <s> BIB006 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> Electricity theft is a major concern for the utilities. With the advent of smart meters, the frequency of collecting household energy consumption data has increased, making it possible for advanced data analysis, which was not possible earlier. We have proposed a temperature dependent predictive model which uses smart meter data and data from distribution transformer to detect electricity theft in an area. The model was tested for varying amounts of power thefts and also for different types of circuit approximations. The results are encouraging and the model can be used for real world application. <s> BIB007 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> E. Scalability <s> 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. <s> BIB008
The number of customers used throughout the research reviewed significantly varies. For example, BIB002 and BIB007 only use less than a few hundred customers in the training. A SVM with a Gaussian kernel is used in BIB002 , for which training is only feasible in a realistic amount of time for up to a couple of ten thousand customers in current implementations BIB005 . A regression model using the Moore-Penrose pseudoinverse BIB001 is used in BIB007 . This model is also only able to scale for up to a couple of ten thousand customers [50] . Models being trained on up to a couple of ten thousand customers include BIB003 and BIB006 using neural networks. The training methods used in those papers usually do not scale to significantly larger customer databases. Larger databases using up to hundreds of thousand or millions of customers are used in  and BIB008 using a SVM with linear Kernel or genetic algorithms, respectively. We believe that a stronger investigation into time complexity of learning algorithms, scalable computing models and technologies such as Apache Spark BIB004 or Google TensorFlow  will allow to efficiently handle Big Data sets for NTL detection. This will also allow to perform the computations in a cloud, requiring researchers to make significantly lower investments in hardware.