Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning

Maizura Mokhtar, Valentin Robu, David Flynn, Ciaran Higgins, Jim Whyte, Caroline Loughran, Fiona Fulton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)
89 Downloads (Pure)


The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive `fit-and-forget' approach to LV network management will be inefficient to ensure its effective operations. A more adaptive approach is required that includes the prediction of risk and capacity of the circuits. Many of the proposed methods require full observability of the networks, motivating the installations of smart meters and advance metering infrastructure in many countries. However, the expectation of `perfect data' is unrealistic in operational reality. Smart meter (SM) roll-out can have its issues, which may resulted in low-likelihood of full SM coverage for all LV networks. This, together with privacy requirements that limit the availability of high granularity demand power data have resulted in the low uptake of many of the presented methods. To address this issue, Deep Learning Neural Network is proposed to predict the voltage distribution with partial SM coverage. The results show that SM measurements from key locations are sufficient for effective prediction of voltage distribution.
Original languageEnglish
Title of host publication9th IEEE International Conference on Innovative Smart Grid Technologies (ISGT Europe)
ISBN (Electronic)9781538682180
Publication statusPublished - 21 Nov 2019


  • Deep learning
  • Low voltage networks
  • Machine learning
  • Predictive models
  • Voltage prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering


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