Data-oriented ensemble predictor based on time series classifiers for fraud detection

Lucas Bastos, Geam Pfeiff, Ramon Oliveira, Helder Oliveira, Maria Emília Tostes, Sherali Zeadally, Eduardo Cerqueira, Denis Rosário

Research output: Contribution to journalArticlepeer-review


Non-Technical Losses (NTL) remain a challenge because electricity theft is prevalent in traditional meters and smart metering systems and structures. They cause financial losses that affect the security of supply and lead to a cooperative burden because NTL are incorporated in the expense tariff and are paid by all customers in countries such as India, China, Brazil, Tunisia, Uruguay, and others. A significant number of methods have been proposed to detect NTL, but they cannot precisely recognize this type of fraud, which enables companies to better identify the fraud's origin and evaluate its financial impact. In addition, it is important to combine several Time-Series Classification (TSC) algorithms which consider the time-dependent nature of energy consumption data. We propose a data-oriented heterogeneous ensemble predictor based on time series classifiers for NTL detection, called DETECT. It classifies energy consumption samples either as honest or as fraudulent and further categorizes the type of fraud which might have been committed based on the analysis of the consumption patterns of each sample. DETECT is a fraud detection algorithm that has the ability to analyze and classify NTL frauds effectively regardless of the dataset. Using our proposed approach, we obtained a performance improvement with a Detection Rate (DR) value equal to 93.45% and a False Positive Rate (FPR) value equal to 1.61%. DETECT focuses on time-series data, which enables the development of a method that better interprets real-world scenarios and it is more error-resistant.

Original languageEnglish
Article number109547
JournalElectric Power Systems Research
StatePublished - Oct 2023

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their valuable comments which helped us improve the content and presentation of this paper. This study was funded in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 . This work is also partially supported by the R&D project entitled “IoT-Cloud de Medição de Energia Centralizada Voltado a Rede CEA”.

Publisher Copyright:
© 2023 Elsevier B.V.


  • Ensemble
  • Fraud detection
  • Machine learning
  • Smart meter

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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