AI future-proofs power network

Press/Media: Research

Description

An award-winning project is having a positive impact on the Scottish electricity network.

 

A good test of the worthiness of an award winner comes from revisiting that project further into its working life. Will the water that has passed under the bridge wash away the gloss? That is certainly not the case with the Networks Constraints Early Warning System (NCEWS), which won the 2019 E&T Innovation of the Year at the IET’s annual Innovation Awards.

This collaboration between Heriot-Watt University and SP Energy Networks (SPEN) used large-scale smart meter / asset data to develop advanced machine-learning algorithms that can extract information about missing cable assets and voltage excursions. This enables high prediction accuracy, even if, as is often the case, some data is missing.

Period24 May 2020

Media contributions

1

Media contributions

  • TitleAI future-proofs power network
    Degree of recognitionNational
    Media name/outletInstitute of Engineering and Technology
    Media typePrint
    Country/TerritoryUnited Kingdom
    Date24/05/20
    DescriptionAn award-winning project is having a positive impact on the Scottish electricity network. A good test of the worthiness of an award winner comes from revisiting that project further into its working life. Will the water that has passed under the bridge wash away the gloss? That is certainly not the case with the Networks Constraints Early Warning System (NCEWS), which won the 2019 E&T Innovation of the Year at the IET’s annual Innovation Awards. This collaboration between Heriot-Watt University and SP Energy Networks (SPEN) used large-scale smart meter / asset data to develop advanced machine-learning algorithms that can extract information about missing cable assets and voltage excursions. This enables high prediction accuracy, even if, as is often the case, some data is missing.
    URLhttps://eandt.theiet.org/content/articles/2020/05/et-innovation-awards-ai-future-proofs-power-network/
    PersonsDavid Flynn, Valentin Robu

Keywords

  • Smart Grid
  • Data Analysis
  • Artifical Intelligence
  • Energy Systems