Improve Utilities’ Vegetation Management Programs

NV5 Geospatial Debuts Predictive Modeling Platform to Improve Utilities’ Vegetation Management Programs

August 25 Webinar to Highlight How Analysis of Lidar and Historical Data Can Help Electric Utilities Improve Reliability and Reduce Costs by Identifying Areas for Proactive Trimming and Removal

HOLLYWOOD, Fla., August 10, 2021 – NV5 Geospatial, powered by Quantum Spatial, North America’s largest geospatial data firm, today announced the launch of Trim Optimization, a predictive modeling platform that enables electric utilities to enhance vegetation management programs with risk-based assessments. Using information derived from existing lidar and historical data, utilities can leverage Trim Optimization to prioritize tree trimming activities by taking into account the risk posed by individual trees and other operational constraints.

“Trees are to blame for a large percentage of outages, and vegetation management is the single biggest cost for electric utilities. Yet, utilities have only started to look at proactive, risk-based management programs, rather than the traditional cycle-based ones,” said Ian Berdie, VP of Innovation for NV5 Geospatial. “NV5 Geospatial’s Trim Optimization platform will help utilities improve grid reliability through better decision making, while also saving them money through greater efficiency and the ability to target areas that have the most potential for problems.”

Vegetation is one of the largest sources of outages, accounting for more than half, according to a recent survey, Geospatial Analytics, Resilience and Extreme Weather Readiness. The majority of respondents also noted that they use data to analyze risk, but budget constraints often prevent them from investing in the data they need.

The trim optimization platform takes a phased approach to identify relative risk to target vegetation management work where it will have the most impact. With extensive expertise, NV5 Geospatial first identifies several attributes associated with vegetation-caused outages that can be modeled from high-density lidar and provide a relative risk score. Utility-specific data, such as historic tree failures information or other factors, can be analyzed to enhance results further. The final risk scores will provide a quantitative assessment of combined risk, enabling utilities to develop work plans that prioritize vegetation management mitigation efforts and result in greater operational efficiency.

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