How Lede Cut Vegetation Management Costs by 5.5 MNOK

In Brief

Lede, one of Norway's largest distribution system operators, manages 18,115 km of power network in an area characterised by fast-growing forests and increasingly frequent storms. Traditional calendar-based vegetation management was leaving the program reactive, costly, and difficult to prioritise. Lede partnered with StormGeo to pilot GridEyes across approximately 250 km of high-risk corridor, moving to condition-based monitoring, risk-driven prioritisation, and validated clearing outcomes. The pilot has opened a path toward more targeted field operations and a projected annual saving of more than 5.5 MNOK.

About Lede

Lede is one of Norway's largest distribution system operators, supplying electricity to more than 218,000 customers across Vestfold, Skien, Porsgrunn, Bamble, Siljan and Hjartdal in Telemark, and Svelvik in Buskerud. The company owns and operates the regional grid in Vestfold and Telemark, delivering more than 7 TWh of electricity annually, of which around 30% is supplied to industrial customers in the Grenland area.

Lede manages a total of 18,115 km of power network across a geography where dense, fast-growing forests create persistent pressure on overhead infrastructure. Keeping that network reliable is not an administrative challenge. It is a daily operational one.

When the calendar stops being enough

Before GridEyes, Lede ran its vegetation management program on a cycle-based model. Inspection and trimming schedules were built around administrative rotation rather than real conditions on the ground. In a region where tree growth accelerates sharply during warm, wet seasons and where convective storms have become more frequent and more damaging, that model had a fundamental problem: it could not tell the difference between a corridor that was genuinely safe and one that was silently becoming a liability.

Clearing work happened, but validation of that work often came years later. The program was reactive by design. When storms arrived, the consequences were visible: outages, material damage, emergency restoration costs, and KILE penalties — the financial penalties applied under Norwegian regulation for interruptions to supply.

The cost of vegetation management across Lede's network ran to approximately 30 MNOK annually. The issue was not whether the investment was being made. It was whether it was being directed to the right places, at the right time.

Where the model fell short:

• No condition-based visibility. Without continuous or high-frequency monitoring, teams could not distinguish high-risk corridors from low-risk ones between scheduled visits.
• Inefficient resource allocation. Field effort was distributed by rotation cycle, not by actual risk level. Effort was not placed where the operational impact was highest.
• Reactive rather than preventive. Outages, restoration, and KILE costs were consequences of a model that discovered problems only after they had developed.
• Limited clearing validation. Trimming and clearing work was difficult to verify systematically, with re-inspection sometimes occurring years after the original cutting.

A breaking point was coming

The case for change had been building over several years. Storms in the Vestfold and Telemark region were arriving with greater frequency and intensity. The combination of more volatile weather and the inherent limitations of a calendar-based program made the reactive consequences of the old model harder to absorb: more outages, more restoration calls, and rising KILE exposure.

Recent advancements in satellite monitoring technology had also changed what was practically available. Higher-frequency coverage, AI-driven change detection, and weather-linked risk insight had matured to the point where a pilot program could generate operationally meaningful outputs, not just data for data's sake.

For Lede, the timing reflected both external pressure and internal readiness. The organisation was experienced enough in its vegetation program to know where the gaps were, and the technology had developed to the point where a condition-based monitoring approach was viable at scale.

A partnership built on the ground

Lede's decision to work with StormGeo grew out of a working relationship built over time through StormGeo's long-standing cooperation with the European Space Agency (ESA) and early GridEyes activities. Rather than a standard market evaluation, the process was driven by established trust and a deep understanding of Lede's operational context. Through close collaboration with Morten Gøytil and Gaute Gjerpen, StormGeo tailored the solution to Lede's specific network conditions and workflows, and supported the development of a clear internal business case demonstrating both the operational and financial value of moving to a condition-based approach.

That closeness of collaboration between the two teams became a defining characteristic of the project, and what moved GridEyes from concept validation to a commercial engagement.

"What gave us confidence in StormGeo was not just the technology. It was the fact that they understood our network, our challenges, and what we actually needed to make better decisions in the field. The business case they helped us build made it straightforward to move forward internally."

Morten Gøytil, Head of Maintenance, Lede

What the program could suddenly see

From calendar to condition

The most significant shift the GridEyes pilot enabled was not a reduction in field activity. It was a change in what directed that activity. Rather than scheduling crews according to administrative rotation, Lede's team could identify which specific corridors showed the highest vegetation proximity, growth rate, or post-storm exposure risk, and prioritise those sections regardless of where they sat in the clearing cycle.

Sensitive areas with elevated risk profiles could be flagged and acted on early. Sections where recent clearing had been effective could be confirmed and deprioritised. The program moved from a coverage model to a risk model.

Seeing the network differently

Full network monitoring, combined with advanced analytics, gave Lede's maintenance team a more complete picture of risk across the grid than was previously achievable through periodic inspection alone. The platform supported condition-based decision-making: where are the highest-risk corridors right now, not where are the corridors that were last inspected longest ago.

Less blind clearing, more precision

The business case for moving from calendar-based to risk-based vegetation management at Lede is grounded in the structure of the existing cost base. With approximately 30 MNOK spent annually on vegetation clearing, even a meaningful reallocation of effort toward higher-risk corridors creates significant headroom for savings.

The pilot identified a net efficiency opportunity of more than 5.5 MNOK per year, achieved through a combination of eliminating redundant control inspections, reducing area-wide and ad-hoc clearing in lower-risk sections, and enabling data-driven selection of risk trees outside the standard clearing zone.

"We were not looking for more data. We were looking for clearer priorities. GridEyes gave us a way to see which corridors genuinely needed attention and which ones could wait — and that is what changes how a vegetation program actually operates on the ground."

Gaute Gjerpen, Head of vegetation, Lede

Ready to see what your vegetation program could look like?

GridEyes helps grid operators across EMEA move from calendar-based inspection to risk-based, intelligence-led vegetation management. If Lede's experience resonates with the challenges your program is facing, the next step is a conversation.

Discover GridEyes or contact our team to discuss what a pilot could look like for your network.