ALVES, H. M. M.; Helem M. M. Alves.; http://lattes.cnpq.br/6052768126824843 Endereço; ALVES, Helem Monyelle de Melo.
Abstract:
Non-technical losses are mainly caused by the electricity irregular consumption due to
fraud or theft. Their reduction is one of the main objectives of electricity distribution
companies. Currently, companies have mainly used in loco inspections to identify
irregular customers. However, these inspections are often associated with high costs and
low effectiveness. Then, many companies have resorted to data mining techniques in
order to increase assertiveness in the selection of irregular customers for inspections,
using cadastral information such as class, supply voltage, type of connection and, mainly,
historical consumption data. In this work, the contribution of attributes derived from the
consumption electric energy history in non-technical losses detection using data mining
techniques is analyzed. Therefore, new attributes are created from the consumption data,
using seasonality characteristics, statistical information, monthly consumption variations,
fall rates and consumption information in the frequency domain. In order to define the
best attributes considering the original attributes and the attributes created subsequently,
the attribute selection methods Correlation Based Feature Selection and Relief are used.
Afterwards, the multilayer perceptron artificial neural networks algorithm is applied to
classify the database clients between regular and irregular using the selected attributes.
From the results, it was verified that the new attributes addition contributed to increase
the artificial neural networks assertiveness, providing approximately a 10 percentage
point gain, which can represent significant savings on the money spent by concessionaires
with not assertive inspections. Therefore, it can be emphasized that the attributes analysis
presented in this work can be used to reduce costs associated with non-technical losses
detection by improving assertiveness in the potential irregular client’s identification.