Keeping the lights on with risk-based vegetation management
Vegetation management is one of the largest preventative maintenance costs for energy providers battling to prevent power outages and wildfire ignition. For years, utilities have relied on time-based vegetation maintenance schedules but artificial intelligence (AI), satellite imaging and big data analytics are leading the way to a more efficient and cost-effective solution. Risk-based vegetation management is transforming the way we manage the right of way, reducing energy costs and protecting our environment and communities.
Harnessing nature with intelligence
Many utilities still take a time-based approach to vegetation management, appointing trimming companies to cut to a schedule to protect overhead power lines. But this doesn’t allow for the changing and unpredictable character of nature. Some areas are over trimmed, while others are at risk from falling trees or overgrowth, leading to increased, unplanned costs. Contractors are often paid on a cost per mile basis, and with 450,000 miles of active electrical transmission lines in North America alone, unnecessary vegetation maintenance could be adding significant amounts to utility bills for homeowners and businesses.
Vegetation management keeps communities safe and prevents wildfires, but poor maintenance planning and over trimming can increase the cost of energy distribution and result in loss of habitat for protected plants and animals.
Risk-based vegetation management brings advanced intelligence to maintenance and cutting schedules. Using a combination of AI algorithms trained to identify vegetation characteristics, along with satellite imagery and weather-based information, predictive models are created to define risk areas. Utility companies can now plan a maintenance schedule that addresses problems before they arise. This condition-based management provides advanced strategic knowledge while detecting risks as they emerge, enabling an actionable targeted approach.
The consequences of mismanagement are serious and the penalties are severe. As a result, regulatory bodies require utility companies to provide evidence of comprehensive and considered approaches to vegetation management. A risk-based approach, with the data and evidence to back it up, demonstrates this commitment.
Predicting and preventing when managing the right of way
20tree.ai vegetation management software uses daily satellite images to give a total view of a power line right of way. High-resolution satellite imagery, combined with AI algorithms, displays powerline sections most at risk and allows utility companies to address current and future hot spots as a priority.
With the most advanced sensor technology, our software detects vegetation changes that are invisible to the naked eye. Different species can be identified and their growth rates predicted. Subtle changes in foliage can detect diseased and dying trees, highlighting areas where trees are more likely to fall and enabling fast, preventative action. Historical and predictive weather data can identify areas most at risk of storms and outages, enabling vegetation management to become a key factor in informed prevention, reducing environmental damage and costs in problem areas.
The power of risk-conditioned technology is in the data, pulling scientific, climate and weather information from multiple sources and combining this with enhanced imagery. By using advanced algorithms to make intelligent decisions, utility companies can combat the rising costs and risks associated with managing the right of way.
