Utilities around the world 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. These wildfires and other extreme weather events also take a significant toll on transmission lines and other utility assets, threatening worker safety and grid reliability.
To address these challenges, in May 2020 – Zurich, Switzerland-based Hitachi Energy launched Lumada Inspection Insights, its end-to-end portfolio of digital solutions for the inspection, monitoring, and optimization of critical assets. These are some sophisticated solutions, relying on AI and machine learning (ML) to analyze a wide spectrum of image types, assets and risks.
One of them, Hitachi Vegetation Manager, the company describes as “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 R&D centers in Japan, takes images of trees and forests from a variety of 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.
“With satellites remotely capturing images and AI analyzing them, we can better optimize and plan for addressing areas of concern,” Bryan Friehauf, SVP of enterprise software solutions at Hitachi Energy, told VentureBeat. “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.”
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. To that end, Hitachi Energy’s solution aims to empower utilities to improve decision making, optimize operations and “as a result, achieve their reliability, safety and sustainability goals.”
Overall, the Hitachi Vegetation Manager gives utilities and government agencies a very accurate prognostic on how the vegetation is growing and this is important for anyone who has to manage vegetation around their linear assets. In other words, some cities could use tech like this.
Takeaway
The solution, which uses algorithms developed at one of the company's R&D centers in Japan, takes images of trees and forests from a variety of 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 and prevent wildfires.
Action point
Hitachi Vegetation Manager is good for cities with a lot of vegetation as single wildfire prevention will easily recoup the cost of the solution. Moreover, it will also help the city plan for the future as well as with ongoing vegetation maintenance. As a public official pushing for the procurement of such a solution, you could potentially benefit in the long run — while at the same time helping your municipality better manage its green resources.
Hitachi Vegetation Manager is good for cities with a lot of vegetation as single wildfire prevention will easily recoup the cost of the solution. So, if your company serves such municipalities, you may want to explore partnership opportunities with Hitachi Energy. What they offer is rather unique and the insights their solution offers could be life-saving, while at the same time helping towns and cities better manage their green resources.