Debunking the Myths of Predictive Utility Vegetation Management

If we don’t see a problem, we often assume there isn’t one.

I encounter that mindset often in the utility industry, particularly when meeting with utility vegetation management (UVM) experts. Many utilities still rely largely on reactive UVM workflows; they tend to only remove hazard trees and other vegetation when they present a genuine threat to infrastructure.

While this may have worked in UVM for many years, times and conditions are changing. Budget constraints are forcing utilities to make difficult investment decisions, while more frequent severe weather events inflict damages that can quickly turn once-safe trees into a reliability risk. To improve both the efficiency and effectiveness of their UVM strategies, utilities need a more predictive approach to vegetation management.

A predictive approach is one that helps utilities understand not only current conditions, but also how vegetation-related risk is likely to evolve over time. Using both lines of sight to present and future state of vegetation enables utilities to resolve vegetation issues before they compound out of control.

Predictive UVM relies on data-driven insights, AI, and machine learning models that analyze imagery like satellite scans and LiDAR to anticipate vegetation growth over time and determine potential risks before they cause outages or wildfires.

Unlike conventional, cycle-based trimming methods, predictive UVM enables utilities to focus resources on high-risk areas, reduce vegetation maintenance costs, and maximize reliability and resilience.

Inspired by conversations with UVM professionals around the world, here are some common misconceptions I’ve heard about predictive vegetation management, and their realities.

In reality, small trees and vegetation, when left unchecked, will become larger and more expensive problems in the future. Disregarding tall-growing species when smaller and reducing stem density increases future workloads, crew demand, and long-term maintenance costs. AI-enabled UVM technology makes it possible to account for the growth rates of certain tree species over time .For example, empress trees, which can grow up to 20 feet per year, or poplars at 10 or more feet per year can be pruned or removed long before they begin encroaching on power assets, at which time the costs and complexity of trimming increase considerably. This is where advanced UVM software solutions shine.

Not necessarily. While pruning may cost less than full tree removal on a per-job basis, repeated pruning of fast-growing trees near conductors can drive up future costs and put system reliability at risk. It’s important for UVM programs to consider trimming-to-removal cost ratios. Removing the tree outright is clearly the most cost-effective option for bending the long-term cost curve for vegetation maintenance.

Advanced UVM software can provide predictive insights like this to help utilities optimize operational expenditures. The most advanced solutions, like GridOS® Visual Intelligence, can analyze imagery and work records to identify:

Utilities may not be able to stop vegetation growth, but they can certainly control how much money they spend curbing it. The goal for a modern UVM program is to optimize O&M expenses and mitigate risk while achieving safe, reliable power delivery.

With predictive vegetation management software, utilities can control current vegetation encroachment, anticipate future work, prioritize work, reduce avoidable repeated work, and curb vegetation expense and tree growth. Programs that plan and manage growth proactively generally outperform programs that respond strictly to active encroachments.

This is one of the more dangerous misconceptions in UVM. Utilities placing all vegetation risks into a single category are putting themselves in danger of severe and prolonged power grid disruptions as well as unnecessary spending. Vegetation risk varies based on several factors:

GridOS Visual Intelligence helps utilities attain these insights via data analysis, ensuring better decisions about pruning and removal. Some insights can further help utilities better understand risk from the span level up to and across the entire network. This type of data analysis makes it much easier for utilities to understand how to prioritize work based on current and future conditions and optimize reliability while balancing their long-term O&M spending.

For more information on creating an advanced, modern UVM program, check out our solution paper on GridOS Visual Intelligence.