Artificial Intelligence: National Grid Uses AI And Satellite Imagery To Save Costs On Pruning Vegetation

September 9, 2022 | Artificial Intelligence, News

National Grid Massachusetts, the gas and electric company, tied up with AiDash, an AI startup, to help streamline some aspects of its operations.

National Grid has saved as much as $2 million on its vegetation management costs after deployment of AiDash’s dashboard that uses AI and satellite imagery to home in on problems such as overgrowing trees, power lines and circuits.

National Grid was struggling with rising, unaffordable costs of managing the vegetation that threatens its 13,500 miles of distribution lines. As a result, there were increasing cases of power disruptions and a forgettable year during which the reliability of its grid fell to a low. (TechTarget)

“With those contractor rates rising, we started deferring the work,” said Bertram Stewart, reliability analytics and vegetation strategy manager at National Grid to TechTarget on tree pruning. “We couldn’t afford to do it.”


National Grid embarked on a proof of concept project with AiDash to test if the latter’s near real-time imagery of tree and shrub conditions along the utility’s electrical lines could red flag those areas that needed immediate attention.

Three months of work with AiDash generated data on:

· which electric circuits to prune based on different criteria

· how many miles of the power line had vegetation next to the wires

· vegetation density

· data on when an individual circuit was last serviced to cut back or remove trees

· and the reliability and performance history of a particular circuit, including the degree of its criticality and how badly it might perform in the next year based on how much vegetation it had around it.

National Grid Then applied the data gained from the proof of concept to its operations for about a year.

According to Stewart, the company saved about $2 million by avoiding unnecessary vegetation work. Furthermore, it turned around its reliability performance in the positive direction.

The technology helped the company refine its analysis based on the AI model from AiDash and the output information it received from them.

Related Story: Satellites and AI Could Together Predict Wildfires Accurately

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