The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> C. Incorrect inspection results <s> We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service. <s> BIB001 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> C. Incorrect inspection results <s> We consider supervised learning in the presence of very many irrelevant features, and study two different regularization methods for preventing overfitting. Focusing on logistic regression, we show that using L1 regularization of the parameters, the sample complexity (i.e., the number of training examples required to learn "well,") grows only logarithmically in the number of irrelevant features. This logarithmic rate matches the best known bounds for feature selection, and indicates that L1 regularized logistic regression can be effective even if there are exponentially many irrelevant features as there are training examples. We also give a lower-bound showing that any rotationally invariant algorithm---including logistic regression with L2 regularization, SVMs, and neural networks trained by backpropagation---has a worst case sample complexity that grows at least linearly in the number of irrelevant features. <s> BIB002 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> C. Incorrect inspection results <s> 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. <s> BIB003 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> C. Incorrect inspection results <s> Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. <s> BIB004 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> C. Incorrect inspection results <s> Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. <s> BIB005 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> C. Incorrect inspection results <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> BIB006
In the preliminary work of BIB006 we noticed that the inspection result labels in the training set are not always correct and that some fraudsters may be labelled as non-fraudulent. The reasons for this may include bribing, blackmailing or threatening of the technician performing the inspection. Also, the fraud may be done too well and it is not observable by technicians. Another reason may be incorrect processing of the data. It must be noted that the latter reason may, however, also label non-fraudulent behavior as fraudulent. Handling noise is a common challenge in machine learning. In supervised machine learning settings, most existing methods address handling noise in the input data. There are different regularization methods such as L 1 or L 2 regularization BIB002 or learning of invariances allowing learning algorithms to better handle noise in the input data  , BIB001 . However, handling noise in the training labels is less commonly addressed in the machine learning literature. Most NTL detection research use supervised methods and this shortcoming of the training data and potential false positive labels in particular are not much reported in the literature, except in BIB003 . Unsupervised methods such as clustering or dimensionality reduction  that totally ignore the labels can be used to overcome this limitation. Also, in many situations, most customers have never been inspected. In a purely supervised training strategy, the unlabeled data is discarded. However, using it may support training. This domain is called semisupervised, for which semi-supervised clustering BIB004 has been proposed. Deep neural networks can be pre-trained using autoencoders or restricted Boltzmann machines BIB005 in order to take advantage of unlabeled data. Both, unsupervised and semi-supervised learning, should be further explored for NTL detection. Furthermore, we are not aware of research that has applied reinforcement learning to NTL detection. We believe that it can be promising to explore in this direction, as reinforcement learning needs only very little supervision in the form of rewards.