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1714174 Vol 9 · Issue 8 Download Paper

Communication-Based Theft Detection Models for Nigeria’s Prepaid Metering Infrastructure

Owoeye Akinyemi Sunday Eseosa Omorogiuwa

Subject area: Science,Engineering and Technology  ·  Area of research: Information and Communication Engineering

DOI: https://doi.org/10.64388/IREV9I8-1714174

Abstract

Electricity theft remains a major contributor to non-technical losses in Nigeria’s power distribution sector despite the widespread deployment of prepaid metering systems. Existing theft-detection approaches are predominantly consumption-based and increasingly ineffective against sophisticated fraud techniques such as communication jamming, token manipulation, and partial meter bypassing. This paper proposes and evaluates a communication-based, multi-model framework for electricity theft detection tailored to Nigeria’s prepaid Advanced Metering Infrastructure (AMI). Four analytical models are developed: the Consumption Pattern Analysis Model (CPAM), the Communication Integrity Model (CIM), the Vending and Token Behaviour Model (VTBM), and the Peer Comparison and Cluster Deviance Model (PCDM). These models are integrated using a risk-based decision framework to improve detection robustness. The proposed approach is evaluated using a real prepaid metering dataset comprising 100 customer records from a Nigerian distribution network. Performance is assessed using accuracy, precision, recall, and F1-score. Results show that communication-based indicators significantly outperform consumption-only analysis, with the integrated framework achieving 90% accuracy and an F1-score of 93%. The findings demonstrate that incorporating communication integrity and vending behaviour analytics into prepaid metering systems offers a practical, scalable solution for detecting electricity theft in developing-country AMI environments.

Keywords

Electricity Theft Detection; Advanced Metering Infrastructure; Communication Integrity; Prepaid Metering; Smart Grid Analytics

References

[1] Abdulsalam, A., Musa, S., Danjuma, A., 2021. Electricity theft and non-technical losses in Nigeria’s power sector. Energy Policy 149, 112018.

[2] Adekunle, A., Wahab, A., 2022. Analysis of prepaid vending anomalies in STS-based metering systems. Electric Power Systems Research 204, 107698.

[3] Ahmed, M., Yusuf, R., Salami, A., 2022. Cyber vulnerabilities in advanced metering infrastructure deployments in developing countries. International Journal of Critical Infrastructure Protection 37, 100495.

[4] Anwar, S., Mahmood, T., Khan, A., 2020. Electricity theft detection using machine learning techniques: A review. Renewable and Sustainable Energy Reviews 119, 109540.

[5] Bello, A., Okafor, C., 2023. Machine learning-based consumption anomaly detection for prepaid electricity meters. Sustainable Energy, Grids and Networks 34, 101012.

[6] Depuru, S., Wang, L., Devabhaktuni, V., 2011. Electricity theft: Overview, issues, prevention and a smart meter based approach. Energy Policy 39(2), 1007–1015.

[7] Gao, J., Liu, Y., Wang, X., 2021. Tamper-event analytics for electricity theft detection in smart grids. IEEE Transactions on Smart Grid 12(4), 3456–3467.

[8] González, J., Sousa, T., Vale, Z., 2021. Multi-source electricity theft detection using AMI data. Electric Power Systems Research 196, 107225.

[9] Gupta, R., Lee, K., 2021. Non-technical losses in power distribution systems: A comprehensive review. Energy Reports 7, 345–358.

[10] Khan, R., Al-Fuqaha, A., Guizani, M., 2022. Security and privacy in smart grid communications: A survey. IEEE Communications Surveys & Tutorials 24(1), 1–31.

[11] Liang, X., Zhang, Y., Niyato, D., 2022. Communication-layer attacks and detection in advanced metering infrastructure. IEEE Transactions on Smart Grid 13(2), 1650–1661.

[12] Nwokolo, C., Musa, S., 2023. Challenges of electricity theft detection in Nigerian distribution networks. Utilities Policy 78, 101401.

[13] Okereke, O., Danladi, A., Bello, S., 2022. Review of electricity theft mitigation strategies in Nigeria. Energy Strategy Reviews 40, 100812.

[14] Olukoju, A., 2021. Electricity theft and regulatory enforcement in Nigeria. Journal of African Energy Studies 6(2), 45–60.

[15] Ogunleye, T., Hassan, M., 2023. Token fraud and security gaps in prepaid electricity metering. International Journal of Electrical Power & Energy Systems 146, 108811.

[16] Otuoze, A., Mustapha, M., Yusuf, S., 2022. Advanced metering infrastructure deployment and challenges in Nigeria. Electric Power Systems Research 210, 108121.

[17] Otuoze, A., Bello, A., Danjuma, I., 2024. Hybrid analytics for electricity theft detection using AMI data. Sustainable Energy, Grids and Networks 39, 101356.

[18] Sousa, T., Vale, Z., González, J., 2022. Integrated clustering and anomaly detection for electricity theft identification. Electric Power Systems Research 201, 107534.

[19] Zhang, Y., Gao, J., Wang, X., 2023. Deep learning-based electricity theft detection using tamper and consumption data. International Journal of Electrical Power & Energy Systems 145, 108640.

How to cite this paper

Owoeye Akinyemi Sunday, Eseosa Omorogiuwa "Communication-Based Theft Detection Models for Nigeria’s Prepaid Metering Infrastructure" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 501-504 https://doi.org/10.64388/IREV9I8-1714174
Owoeye Akinyemi Sunday, Eseosa Omorogiuwa "Communication-Based Theft Detection Models for Nigeria’s Prepaid Metering Infrastructure" Iconic Research And Engineering Journals, vol. 9, no. 8, Feb. 2026, doi: https://doi.org/10.64388/IREV9I8-1714174
Owoeye Akinyemi Sunday, Eseosa Omorogiuwa (2026). Communication-Based Theft Detection Models for Nigeria’s Prepaid Metering Infrastructure. Iconic Research And Engineering Journals, 9(8). doi: https://doi.org/10.64388/IREV9I8-1714174
Owoeye Akinyemi Sunday, Eseosa Omorogiuwa "Communication-Based Theft Detection Models for Nigeria’s Prepaid Metering Infrastructure" Iconic Research And Engineering Journals, vol. 9, no. 8, Feb. 2026. Crossref, https://doi.org/10.64388/IREV9I8-1714174
@article{1714174,
      author = {Owoeye Akinyemi Sunday, Eseosa Omorogiuwa},
      title = {Communication-Based Theft Detection Models for Nigeria’s Prepaid Metering Infrastructure},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {8},
      pages = {501-504},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714174.pdf},
      abstract = {Electricity theft remains a major contributor to non-technical losses in Nigeria’s power distribution sector despite the widespread deployment of prepaid metering systems. Existing theft-detection approaches are predominantly consumption-based and increasingly ineffective against sophisticated fraud techniques such as communication jamming, token manipulation, and partial meter bypassing. This paper proposes and evaluates a communication-based, multi-model framework for electricity theft detection tailored to Nigeria’s prepaid Advanced Metering Infrastructure (AMI). Four analytical models are developed: the Consumption Pattern Analysis Model (CPAM), the Communication Integrity Model (CIM), the Vending and Token Behaviour Model (VTBM), and the Peer Comparison and Cluster Deviance Model (PCDM). These models are integrated using a risk-based decision framework to improve detection robustness. The proposed approach is evaluated using a real prepaid metering dataset comprising 100 customer records from a Nigerian distribution network. Performance is assessed using accuracy, precision, recall, and F1-score. Results show that communication-based indicators significantly outperform consumption-only analysis, with the integrated framework achieving 90% accuracy and an F1-score of 93%. The findings demonstrate that incorporating communication integrity and vending behaviour analytics into prepaid metering systems offers a practical, scalable solution for detecting electricity theft in developing-country AMI environments.},
      keywords = {Electricity Theft Detection; Advanced Metering Infrastructure; Communication Integrity; Prepaid Metering; Smart Grid Analytics},
      month = {February},
      doi = {https://doi.org/10.64388/IREV9I8-1714174}
  }