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Vegetation Management for
Electric Grids

Empowering you to run cost-effective, resilient grids.

Revolutionary vegetation management solution by combining space-level AI, and satellite insights.

The GridEyeS project, conducted with HVL, eSmart, European Space Agency (ESA), ENTSO-e, and StormGeo, is researching and developing a new satellite and drone technology approach to vegetation management.

Solar panel

Goodbye to manual inspections, hello to cost-effective, resilient grids.

Highlights

  • Move beyond rigid calendars with AI-driven risk assessments to your grid.
  • Prioritize maintenance efforts efficiently based on high-risk areas.
  • Reduce inspection and vegetation management costs.
  • Retrieve state-of-the-art weather forecasts to anticipate grid stressors.
  • Ensure timely maintenance through accurate load predictions.

Key benefits

Accurate Risk Assessments

Understand grid vulnerabilities proactively, anticipate potential disruptions and take preventative measures. 

Faster Outage Management

Minimize downtime and restore power efficiently whilst at all times swiftly responding during grid disruptions.

Improved Restoration Strategies

Plan restoration efforts effectively after events like storms or fires. 

Accurate Risk Assessments

Understand grid vulnerabilities proactively, anticipate potential disruptions and take preventative measures. 

Faster Outage Management

Minimize downtime and restore power efficiently whilst at all times swiftly responding during grid disruptions.

Improved Restoration Strategies

Plan restoration efforts effectively after events like storms or fires. 

Smart Inspections - Safeguarding your business, communities and ecosystems.

Instead of rigid calendars, our AI-driven risk assessments prioritize maintenance based on high-risk areas. Manual inspections are replaced with data-driven efficiency. 

Tree risk identification

Outage management

Outage prediction relies on a sophisticated predictive model that utilizes historical outage data and comprehensive weather records. By analyzing registered outage data alongside detailed historical weather patterns, our model identifies correlations between weather conditions and outage occurrences.

Using advanced machine learning techniques, the model extrapolates from past instances to accurately forecast future outages. It operates at a granular level, providing predictions for outages up to three days in advance, with six-hour intervals. This proactive approach empowers our clients to preemptively manage risks, optimize resource allocation, and enhance operational resilience, ultimately contributing to a more reliable infrastructure and service delivery.

preemptively manage risks

 Integrated Expertise

Our solution incorporates real-time weather forecasts based on our bespoke models. A priority axis for our research are satellite images in order to analyze vegetation impact on grid risk levels. 

Tree identification
"Using satellite images to analyze and predict the influence of vegetation on the risk level in power grids is a high-priority research axis, as vegetation is a main factor for grid disturbance. Furthermore, the potential for using satellite-based information is broad and could, for example, also be exploited for subsidence detections along power line trails.."

Knut H. H. Johansen CEO
eSmart Systems

GOES satellite

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