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Development of a new big data-driven overhead power grid pre-storm damage prediction system


Added by:   National contact point
Added on:   01 Oct 2025
Updated by:   OECD analyst
Updated on:   25 Dec 2025

The Big Data-Driven Overhead Power Grid Pre-Storm Damage Prediction System in Latvia uses AI and machine learning to forecast potential storm damage to power grids. It provides real-time predictions for medium- and low-voltage networks. The system helps grid operators act proactively to prevent power outages.

Name in original language

Jaunas lielo datu bāzētas augstsprieguma elektrotīkla bojājumu prognozēšanas sistēmas pirms vētras izstrāde

Initiative overview

This initiative focuses on developing a modular, scalable AI-powered system to predict damage to overhead power grids caused by storms. By leveraging big data and machine learning algorithms, the system can generate real-time forecasts of potential failures in medium- and low-voltage grids, helping operators plan preventive actions.

Key objectives include:

  • Predicting storm-related damage to power infrastructure before it occurs
  • Supporting proactive decision-making by grid operators to reduce outages
  • Improving resilience of the electrical grid through AI-driven monitoring and alerts

The system is designed to be modular and scalable, allowing it to adapt to different grid configurations and to expand as new data sources become available. By integrating advanced analytics with operational planning, the initiative strengthens energy reliability and disaster preparedness.

Name of responsible organisation (in English)

Latvian Electricity Transmission System Operator

About the policy initiative


Organisation:

  • Latvian Electricity Transmission System Operator

Category:

  • AI policy initiatives, programmes and projects

Initiative type:

  • AI use cases/projects in the public sector

Status:

  • Active

Start Year:

  • 2024

Binding:

  • Non-binding

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