A Data Mining Based NTL Analysis Method

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, Naive 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.

I. Introduction

Having a knowledge of customer behaviour is becoming increasingly important in the electricity industry, especially in deregulated markets. By having this crucial knowledge, utility providers can develop new strategies and products that optimally serve customer demands provide the means of offering the best possible services and maximum value added so as to differentiate themselves from their competitors.