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The massive, beautiful tree canopies in the Western U.S., which may grow perilously close to power lines, can quickly spark destructive wildfires. In fact, 70% of electrical outages are caused by vegetation, and this number has increased by 19% year over year from 2009-2020. The second-largest wildfire in California’s history, The Dixie Fire, sparked when power lines came into contact with a fir tree.
Could AI-driven solutions help prevent wildfires before they start by analyzing the tree growth that can spark them? Hitachi Energy, the Zurich, Switzerland-based global technology company, says yes.
AI is critical for sustainable energy future
Hitachi Energy, formerly known as Hitachi ABB Power Grids (the name was changed last October) is currently focused on “powering good for a sustainable energy future.” One area of concern was how to position itself to serve customers beyond the grid, helping industries that have connecting assets spread across large geographic areas including energy, telecommunications, water, gas pipeline and rail. That includes utilities dealing with thousands of miles of growing vegetation.
“These industries all have similar issues with managing their miles and miles of assets,” Bryan Friehauf, SVP of enterprise software solutions at Hitachi Energy, told VentureBeat. “For instance, you need to keep trees off the railway tracks and roads, as well as away from gas pipelines and other critical infrastructure.”
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Three trends have made the use of geospatial and AI-powered technology critical, he explained: aging infrastructure, siloed systems and climate change. “It can be hard or dangerous to view or manage assets with these conditions,” he said.
Inspecting trees to prevent wildfires and outages
To address these challenges, today Hitachi Energy announced a new AI-driven solution, Hitachi Vegetation Manager, part of the company’s new Lumada Inspections Insights offering. The company claims it is “the first of its kind, closed-loop vegetation resource planning solution that leverages artificial intelligence and advanced analytics to improve the accuracy and effectiveness of an organization’s vegetation job activities and planning efforts.”
The solution, which uses algorithms developed at one of the company’s research and development centers in Japan, takes images of trees and forests from a variety of visual sources, including photo, video, and imagery from industry-leading Maxar satellites. By combining the images with climate, ecosystem and cut plan data as well as machine learning algorithms, Hitachi Vegetation Manager provides utilities with grid-wide visibility and better insights so that organizations can optimize decision-making.
Satellites capture images, AI analyzes them
“With satellites remotely capturing images and AI analyzing them, we can better optimize and plan for addressing areas of concern,” said Friehauf. “This will also reduce the cost and emissions of the management program by minimizing truck and helicopter trips, and ultimately minimize outages and fires caused by vegetation.”
Using AI to track and analyze vegetation is particularly essential for utilities around the world, which are dealing with unprecedented climate-related challenges. In 2021, global wildfires generated an estimated total of 6,450 megatons of CO2 equivalent – approximately 148% more than the EU’s total fossil fuel emissions in 2020.
According to John Villali, research director at IDC Energy Insights, inspection, planning and monitoring are “among the most critical tasks utilities undertake to maintain grid reliability and resiliency. Hitachi Energy’s AI-driven solution, he explained, empowers utilities to improve decision making, optimize operations and “as a result, achieve their reliability, safety and sustainability goals.”
Utility industry more readily adopting AI
Historically, as a highly regulated sector, the utility industry hasn’t been a leader AI and other emerging technologies, said Phil Gruber, general manager in the energy/industry utility practice at Hitachi Vantara, Hitachi’s IT service management company. “The utility industry tends to be very cautious for good reason, and aren’t generally leaders in the use of technology, but they are starting to dabble,” he said.
One issue is organizations often believe they don’t have enough good-quality data to get started in AI or ML. “A lot of our discussion with customers is about trying to meet them where they are at with the data sets they have,” said Gruber. “We often discover they have sufficient data to really improve their decision-making and outcomes.”
But Hitachi Energy’s solution means utilities no longer need arborists to walk around miles of transmission lines to identify every species, Friehauf explained. Once species data is fed into the model, including the location and details such as soil quality, the algorithm can take weather precipitation data, analyze the tree species growth profile and predict where growth will happen and not happen.
“Of course, precipitation isn’t homogenous, so you can have areas even within the same county that receive more precipitation than another,” Friehauf said. “The tool will be able to show that even though you’ve trimmed back certain vegetation, maybe you have to do it again sooner because it gets a lot of rain, or if you’re in a drought you need to know how drought-resistant a species is.”
Overall, the Hitachi Vegetation Manager “gives you a very accurate prognostic on how that vegetation is growing,” said Friehauf. “This is important not just to utilities dealing with wildfire or power outage risks, but anyone who has to manage vegetation around their linear assets.”
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