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Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data

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  • Dokur, Emrah
  • Erdogan, Nuh
  • Sengor, Ibrahim
  • Yuzgec, Ugur
  • Hayes, Barry P.

Abstract

The widespread adoption of low-carbon technologies, such as photovoltaics, electric vehicles, heat pumps, and energy storage units introduces challenges to distribution network congestion and power quality, particularly raising concerns about voltage stability. Enhanced voltage visibility in low-voltage networks is increasingly vital for active grid management, making efficient voltage forecasting tools essential. This study introduces a novel data-driven approach for forecasting node voltages in low-voltage networks with high penetration of low-carbon technologies. Using time series of power measurements from smart meter data, the study integrates an Extreme Learning Machine with the Single Candidate Optimizer to enhance computational efficiency and forecasting accuracy. The model is validated using smart meter datasets from two different low-voltage networks with low-carbon technologies and is compared with several established machine learning models. The results demonstrate that the optimization algorithm significantly improves the tuning of model parameters, achieving up to a 17-fold reduction in computation time compared to the fastest metaheuristic methods implemented. The proposed model demonstrated superior accuracy, with an average voltage deviation of 0.56%. Although the computation time per node achieved is not yet suitable for real time applications, the study shows that the optimization method significantly improves the performance of the forecasting tool.

Suggested Citation

  • Dokur, Emrah & Erdogan, Nuh & Sengor, Ibrahim & Yuzgec, Ugur & Hayes, Barry P., 2025. "Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data," Applied Energy, Elsevier, vol. 384(C).
    • Handle: RePEc:eee:appene:v:384:y:2025:i:c:s0306261925001631
    DOI: 10.1016/j.apenergy.2025.125433
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    References listed on IDEAS

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    1. Meunier, Simon & Protopapadaki, Christina & Baetens, Ruben & Saelens, Dirk, 2021. "Impact of residential low-carbon technologies on low-voltage grid reinforcements," Applied Energy, Elsevier, vol. 297(C).
    2. Liu, Yanli & Wang, Junyi, 2022. "Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 312(C).
    3. Ahmed, Faraedoon & Al Kez, Dlzar & McLoone, Seán & Best, Robert James & Cameron, Ché & Foley, Aoife, 2023. "Dynamic grid stability in low carbon power systems with minimum inertia," Renewable Energy, Elsevier, vol. 210(C), pages 486-506.
    4. Guerra, K. & Gutiérrez-Alvarez, R. & Guerra, Omar J. & Haro, P., 2023. "Opportunities for low-carbon generation and storage technologies to decarbonise the future power system," Applied Energy, Elsevier, vol. 336(C).
    5. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    6. Lazo, Joaquín & Watts, David, 2024. "Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
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