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. Index Terms-Classification techniques, extreme machine learning (ELM), nontechnical losses (NTL), support vector machine (SVM).

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This paper presents a method of determining which type of data provides maximum accuracy with reference to non-technical loss analysis in the electricity distribution sector. The method is based on two popular classification algorithms, Naïve Bayesian and Decision Tree. It involves extracting the patterns of customers' kWh consumption behaviour from historical data and arranging the data in various ways by averaging them yearly, monthly, weekly, and daily. Both techniques are used and compared. The intention is to ensure the acquisition of optimum results in developing representative load profiles to be used as the reference for non-technical loss analysis directed at detecting any significant activities that may contribute to such losses.

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

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