The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> B. Neural networks <s> This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the modern computational technique extreme learning machine (ELM). Nontechnical losses represent a significant proportion of electricity losses in both developing and developed countries. The ELM-based approach presented here uses customer load-profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. This approach provides a method of data mining for this purpose, and it involves extracting patterns of customer behavior from historical kWh consumption data. The results yield classification classes that are used to reveal whether any significant behavior that emerges is due to irregularities in consumption. In this paper, ELM and online sequential-ELM (OS-ELM) algorithms are both used to achieve an improved classification performance and to increase accuracy of results. A comparison of this approach with other classification techniques, such as the support vector machine (SVM) algorithm, is also undertaken and the ELM performance and accuracy in NTL analysis is shown to be superior. <s> BIB001 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> B. Neural networks <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> BIB002 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> B. Neural networks <s> This work presents two computational system for fraud detection for both high and low voltage electrical energy consumers based on data mining. This two kinds of consumers demanded different approaches and methodologies. The first is based on SOM (Self-Organizing Maps), which allows the identification of the consumption profile historically registered for a consumer, and its comparison with present behavior. The second is based on a hybrid of data mining techniques. From the consumer behavior pre-analysis, electrical energy companies will better direct its inspections and will reach higher rates of correctness. The validation and results showed that the two systems are efficient on the cases of lower consumption resulted by fraud. <s> BIB003 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> B. Neural networks <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> BIB004
Neural networks  are loosely inspired by how the human brain works and allow to learn complex hypotheses from data. An ensemble of five neural networks (NN) is trained on samples of a data set containing~20K customers in BIB002 . Each neural network uses features calculated from the consumption time series plus customer-specific pre-computed attributes. A precision of 0.626 and an accuracy of 0.686 are obtained on the test set. A data set of~22K customers is used in BIB004 for training a neural network. It uses the average consumption of the previous 12 months and other customer features such as location, type of customer, voltage and whether there are meter reading notes during that period. On the test set, an accuracy of 0.8717, a precision of 0.6503 and a recall of 0.2947 are reported. Extreme learning machines (ELM) are one-hidden layer neural networks in which the weights from the inputs to the hidden layer are randomly set and never updated. Only the weights from the hidden to output layer are learned. The ELM algorithm is applied to NTL detection in meter readings of 30 minutes in BIB001 , for which a test accuracy of 0.5461 is reported. A self-organizing map (SOM) is a type of unsupervised neural network training algorithm that is used for clustering. SOMs are applied to weekly customer data of 2K customers consisting of meter readings of 15 minutes in BIB003 . This allows to cluster customers' behavior into fraud or non-fraud. Inspections are only carried out if certain hand-crafted criteria are satisfied including how well a week fits into a cluster and if no contractual changes of the customer have taken place. A test accuracy of 0.9267, a test precision of 0.8526, and test recall of 0.9779 are reported.