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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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Date
2025
Authors
Dokur, Emrah
Erdogan, Nuh
Sengor, Ibrahim
Yüzgeç, Uğur
Hayes, Barry P.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
The widespread adoption of low carbon technologies (LCTs) 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 (LV) networks is increasingly vital for an active grid management, making efficient voltage forecasting tools essential. This study introduces a novel data-driven approach for forecasting node voltages in LCT-enriched LV distribution networks. Using time series of power measurements from smart meter data, the study integrates an Extreme Learning Machine (ELM) with the Single Candidate Optimizer (SCO) to enhance both computational efficiency and forecasting accuracy. The model is validated using a realistic LCT-enriched LV network dataset and benchmarked against several established machine learning models. Results demonstrate that the SCO algorithm effectively optimizes ELM 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%. While the computation time per node achieved is not yet suitable for real-time applications, the study proves that SCO significantly enhances ELM performance.
Description
Article peer-reviewed
Keywords
Low carbon technologies , Machine learning , Meta-heuristic , Single candidate optimizer , Smart meter , Voltage forecasting
Citation
Dokur, E., Erdogan, N., Sengor, I., Yüzgeç, U. and Hayes, B. P. (2025) 'Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data', Applied Energy.