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A Data-Driven Power Loss Pattern Recognition for Sustainable Energy Management

by Udoinyang G. Inyang, Emmanuel A. Ubong, Enefiok A. Etuk, Ugboaja S. Gregory, Blessing E. Akponome
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 131
Year of Publication: 2026
Authors: Udoinyang G. Inyang, Emmanuel A. Ubong, Enefiok A. Etuk, Ugboaja S. Gregory, Blessing E. Akponome
10.5120/ijca734c17e3d644

Udoinyang G. Inyang, Emmanuel A. Ubong, Enefiok A. Etuk, Ugboaja S. Gregory, Blessing E. Akponome . A Data-Driven Power Loss Pattern Recognition for Sustainable Energy Management. International Journal of Computer Applications. 187, 131 ( Aug 2026), 10-17. DOI=10.5120/ijca734c17e3d644

@article{ 10.5120/ijca734c17e3d644,
author = { Udoinyang G. Inyang, Emmanuel A. Ubong, Enefiok A. Etuk, Ugboaja S. Gregory, Blessing E. Akponome },
title = { A Data-Driven Power Loss Pattern Recognition for Sustainable Energy Management },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 131 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 10-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number131/a-data-driven-power-loss-pattern-recognition-for-sustainable-energy-management/ },
doi = { 10.5120/ijca734c17e3d644 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:54:29.165539+05:30
%A Udoinyang G. Inyang
%A Emmanuel A. Ubong
%A Enefiok A. Etuk
%A Ugboaja S. Gregory
%A Blessing E. Akponome
%T A Data-Driven Power Loss Pattern Recognition for Sustainable Energy Management
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 131
%P 10-17
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Power loss remains a major challenge in smart grids (SGs), making accurate prediction models vital for sustainable energy management. This study employs deep neural networks (DNNs) to predict power loss using key attributes such as temperature, grid load, and environmental factors. Principal component analysis (PCA) identified grid temperature and load as the most influential variables, contributing 40.6% and 15.93% of the total variance, respectively, with selected features accounting for 76.28% overall. Comparative evaluation of DNN architectures showed that the 6-layer model outperformed configurations with fewer layers, achieving an R² of 94.5%, the lowest MSE (1.00E-03), RMSE (3.40E-02), and MAPE (4.83%). Although it required slightly longer processing time, its superior predictive accuracy justified its selection. Pearson correlation analysis revealed weak positive relationships between temperature, voltage, and power loss, while regional analysis demonstrated that rising temperatures increase consumption and losses. Overall, the results demonstrate that the proposed DNN-based approach provides a robust and data-driven solution for power loss prediction, supporting improved grid efficiency and sustainability, with future work focusing on real-time data integration and additional environmental factors.

References
  1. Agupugo, N. C. P., Kehinde, N. H. M., & Manuel, N. H. N. N. (2024). Optimization of microgrid operations using renewable energy sources. Engineering Science & Technology Journal, 5(7), 2379–2401. https://doi.org/10.51594/estj.v5i7.1360
  2. Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A. S., & Alshamrani, S. S. (2022). Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334–361. https://doi.org/10.1016/j.egyr.2021.11.256
  3. Barja-Martinez, S., Aragüés-Peñalba, M., Munné-Collado, Í., Lloret-Gallego, P., Bullich-Massagué, E., & Villafafila-Robles, R. (2021). Artificial intelligence techniques for enabling Big Data services in distribution networks: A review. Renewable and Sustainable Energy Reviews, 150, 111459. https://doi.org/10.1016/j.rser.2021.111459
  4. Behbahani, M. R., Jalilian, A., & Fini, A. S. (2024). Reconfiguration of distribution network for improving power quality indexes with flexible lexicography method. Electric Power Systems Research, 230, 110172. https://doi.org/10.1016/j.epsr.2024.110172
  5. Butt, O. M., Zulqarnain, M., & Butt, T. M. (2020). Recent advancement in smart grid technology: Future prospects in the electrical power network. Ain Shams Engineering Journal, 12(1), 687–695. https://doi.org/10.1016/j.asej.2020.05.004
  6. Hafezi, R., & Alipour, M. (2020). Sustainable Energy Management. In Encyclopedia of the UN sustainable development goals (pp. 1–13). https://doi.org/10.1007/978-3-319-71057-0_3-1
  7. Huda, A. S. N., & Živanović, R. (2017). Large-scale integration of distributed generation into distribution networks: Study objectives, review of models and computational tools. Renewable and Sustainable Energy Reviews, 76, 974–988. doi:10.1016/j.rser.2017.03.069
  8. Igbogidi, O. N. and Dahunsi, A. A.(2023). Modern Trend of Load Flow Analysis in Power System. American Journal of Engineering Research (AJER) e-ISSN: 2320-0847 p-ISSN : 2320-0936 Volume-12, Issue-4, pp-53-58
  9. Inyang, U. G., Eyoh, I. J., Umoh, U. A., Ubong, E. A., Ene, E. E., Obiyo, D. C., & Akponome, B. E. (2025). Optimal deep neural network parameters for power loss minimization analytics. Nigerian Journal of Technology, 44(4), 647–659. https://doi.org/10.4314/njt.2025.4911
  10. Iyaniwura, A. A., & Mayaki, C. S. (2025). Artificial Intelligence-enabled smart grid systems for real-time load forecasting, fault detection, renewable energy integration and optimization. Global Journal of Engineering and Technology Advances, 24(03), 191-208.
  11. Komolafe, O., & Udofia, K. (2020). Review of electrical energy losses in Nigeria. Nigerian Journal of Technology, 39(1), 246–254. https://doi.org/10.4314/njt.v39i1.28
  12. Luthander, R., Widén, J., Nilsson, D., & Palm, J. (2015). Photovoltaic self-consumption in buildings: A review. Applied Energy, 142, 80-94. doi: 10.1016/j.apenergy.2014.12.028.
  13. Mimi, S., Maissa, Y. B., & Tamtaoui, A. (2023). Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study. Smart Cities, 6(4), 1630–1662. https://doi.org/10.3390/smartcities6040077
  14. Pius J. I, Imo E. N, Ekom E. O.,(2024). Voltage Stability Improvement in The NigerianSouthern 330kV Power System Network with UPFC FACTSDevice. American Journal of Engineering Research (AJER) e-ISSN: 2320-0847 p-ISSN : 2320-0936 Volume-13, Issue-2, pp-27-33
  15. Ratlamwala, T. A. H., & Dincer, I. (2018). 5.8 Sustainable Energy Management. Comprehensive Energy Systems, 315–350. doi:10.1016/b978-0-12-809597-3.00522-8
  16. Rehmani, M. H., Rachedi, A., Erol-Kantarci, M., Radenkovic, M., & Reisslein, M. (2016). Cognitive radio based smart grid: The future of the traditional electrical grid. Ad Hoc Networks, 41, 1–4. https://doi.org/10.1016/j.adhoc.2016.02.010
  17. Saeed, M. S., Mustafa, M. W., Hamadneh, N. N., Alshammari, N. A., Sheikh, U. U., Jumani, T. A., Khalid, S. B. A., & Khan, I. (2020). Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review. Energies, 13(18), 4727. https://doi.org/10.3390/en13184727
  18. Samuel B and Godwin Norense O. A. (2018). Traditional Vs Smart Electricity Metering Systems: A Brief Overview. Journal of Marketing and Consumer Research ISSN 2422-8451 An International Peer-reviewed Journal Vol.46
  19. Selvam, M. M., Gnanadass, R., & Padhy, N. (2016). Initiatives and technical challenges in smart distribution grid. Renewable and Sustainable Energy Reviews, 58, 911–917. https://doi.org/10.1016/j.rser.2015.12.257
  20. Shukla, P. K., & Deepa, K. (2024). Deep learning techniques for transmission line fault classification–A comparative study. Ain Shams Engineering Journal, 15(2), 102427.
  21. Syed, D., Abu-Rub, H., Refaat, S. S., & Xie, L. (2020). Detection of Energy Theft in Smart Grids using Electricity Consumption Patterns. 2020 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata50022.2020.937
  22. Thakur, J., and Chakraborty, B. (2014). Intelli-grid: Moving towards automation of electric grid in India. Renewable and Sustainable Energy Reviews, 42, 16–25. https://doi.org/10.1016/j.rser.2014.09.043
  23. Tran, T., Ngoc, D. V., & Anh, N. T. (2020). Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm. Complexity, 2020, 1–15. https://doi.org/10.1155/2020/2353901
  24. Uzondu, N. N. C., & Lele, N. D. D. (2024). Comprehensive analysis of integrating smart grids with renewable energy sources: Technological advancements, economic impacts, and policy frameworks. Engineering Science & Technology Journal, 5(7), 2334–2363. https://doi.org/10.51594/estj.v5i7.1347
  25. Williams, B., Bishop, D., Gallardo, P., & Chase, J. G. (2023). Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations. Energies, 16(13), 5155. https://doi.org/10.3390/en16135155
  26. Wu, A., & Ni, B. (2015). Line Loss Analysis and Calculation of Electric Power Systems. https://doi.org/10.1002/9781118867273
  27. Youssef, A. M. a. R. (2023). The Role of Nuclear Power in Sustainability of the Electricity Sector. American Journal of Environmental and Resource Economics. https://doi.org/10.11648/j.ajere.20230802.11
  28. Zhang, J. (2023). Energy Management System: The Engine for Sustainable Development and Resource Optimization. Highlights in Science Engineering and Technology, 76, 618–624. https://doi.org/10.54097/cvfd9m83.
  29. Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109, 85–101. https://doi.org/10.1016/j.rser.2019.04.021
Index Terms

Computer Science
Information Sciences

Keywords

DNN Activation Function Smart Grid Power Loss Performance Metrics Correlation

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