How Neara uses artificial intelligence to protect utilities from extreme weather

Over the past few decades, extreme weather events have not only become more severe, but also occur with increasing frequency. Neara helps utility companies and energy providers create models of their power grids and anything that might affect them, such as wildfires or floods. The Redfern, New South Wales, Australia-based startup recently launched an artificial intelligence and machine learning product that can create large-scale network models and assess risk without the need for manual investigation.

Since commercial launch in 2019, Neara has raised a total of A$45 million (approximately US$29.3 million) in funding from investors including Square Peg Capital, Skip Capital and Press Ventures. Its customers include Essential Energy, Endeavor Energy, and SA Power Networks. It also partners with Southern California Edison and EMPACT Engineering.

Neara’s AI and machine learning-based capabilities are already part of its technology stack and are used by utilities around the world, including Southern California Edison, SA Power Networks and Endeavor Energy in Australia, ESB in Ireland and Scottish Power .

Co-founder Jack Curtis told TechCrunch that billions of dollars are spent on utility infrastructure, including maintenance, upgrades and labor costs. When something goes wrong, consumers are immediately affected. When Neara began integrating artificial intelligence and machine learning capabilities into its platform, it did so by analyzing existing infrastructure without manual inspection, which he says is often inefficient, inaccurate and expensive.

Neara then developed its artificial intelligence and machine learning capabilities so it could create large-scale models of utility networks and surrounding environments. Models are used for a variety of purposes, including simulating the impact of extreme weather on electricity supplies before, after, and during events. This increases power restoration speed, keeps utility teams safe and mitigates the impact of weather events.

“The increase in frequency and severity of severe weather is motivating our product development more than any single event,” Curtis said. “Recently, there has been an increase in severe weather events around the world and power grids are being impacted by this phenomenon.” Some examples include Storm Isa, which left tens of thousands without power in the UK, winter storms that caused widespread power outages in the US and left Queensland with a fragile grid Australian tropical cyclone storm.

By using artificial intelligence and machine learning, Neara’s digital model of utility networks prepares energy providers and utility companies. Some of the scenarios Niela can predict include strong winds that could cause power outages and wildfires, flood levels that mean the network needs to shut down its energy source, and ice and snow accumulation that could reduce the reliability and resiliency of the network.

In terms of model training, Curtis said that artificial intelligence and machine learning have been “integrated into digital networks from the beginning,” and lidar is critical to Neara’s ability to accurately simulate weather events. He added that its artificial intelligence and machine learning models are “trained on more than a million miles of diverse network regions, which helps us capture seemingly small but impactful nuances with ultra-accuracy.”

This is important because in situations such as flooding, a single degree difference in elevation geometry can result in inaccurate modeling of water levels, meaning utility companies may need to power power lines before they are needed, or on the other hand, keep Power is taking longer than normal. safe.

Neara co-founders Daniel Danilatos, Karamvir Singh and Jack Curtis

LiDAR images are captured by utility companies or third-party capture companies, not LiDAR. Some customers scan their networks, constantly feeding new data into Neara, while others use it to gain new insights from historical data.

“A key outcome of ingesting lidar data is the creation of a digital twin,” Curtis said. “That’s the power of raw lidar data.”

A few examples of Niela’s work include Southern California Edison, which aims to “autoprescribe,” or automatically identify where vegetation is likely to catch fire more accurately than manual surveys. It can also help inspectors tell investigative teams where to go without putting them at risk. Because utility networks are often large, different inspectors are sent to different areas, which means multiple sets of subjective data. Curtis said using Neara’s platform can make the data more consistent.

In the case of Southern California Edison, Neara used lidar and satellite imagery to model the factors that cause wildfires to spread through vegetation, including wind speed and ambient temperature. But making predicting vegetation risks even more complicated is that regulatory requirements require Southern California Edison to answer more than 100 questions for each of its poles and to inspect its transmission system annually.

In the second example, Neara began working with Australia’s SA Power Networks following the 2022-2023 Murray River flood crisis, which affected thousands of homes and businesses and was considered one of southern Australia’s worst natural disasters. one. SA Power Networks captured LiDAR data from the Murray River region and used Neara to perform digital flood impact modeling and understand how much of its network was damaged and what the remaining risk was.

This allowed SA Power Networks to complete a report analyzing 21,000 power line spans within the flood zone in 15 minutes, a process that would have taken months. As a result, SA Power Networks was able to re-energize the power lines within five days, compared to its initial estimate of three weeks.

3D modeling also enables SA Power Networks to simulate the potential impact of different flood levels on parts of its distribution lines and predict when and where wires may breach clearances or be at risk of losing power. After river levels returned to normal, SA Power Networks continued to use Neara’s model to help plan the reconnection of power supplies along the river.

Neara is currently doing more machine learning R&D. One of the goals is to help utilities get more value from existing live and historical data. It also plans to increase the number of sources available for modeling, with a focus on image recognition and photogrammetry.

The startup is also developing new capabilities with Essential Energy to help utilities assess every asset on their network, including poles. Two factors are currently used to evaluate personal assets: the likelihood that events such as extreme weather will occur and how well they can withstand those conditions. Curtis said this type of risk/value analysis is often performed manually and sometimes doesn’t protect against failures, such as when power outages occurred during the California wildfires. Essential Energy plans to use Neara to develop digital network models that will enable more accurate analysis of assets and reduce risks during wildfires.

“Essentially, we’re keeping utilities one step ahead of extreme weather by understanding exactly how it will impact their networks, allowing them to keep power on and communities safe,” Curtis said.

Source link

from Tech Empire Solutions https://techempiresolutions.com/how-neara-uses-artificial-intelligence-to-protect-utilities-from-extreme-weather/
via https://techempiresolutions.com/

from Tech Empire Solutions https://techempiresolutions.wordpress.com/2024/02/16/how-neara-uses-artificial-intelligence-to-protect-utilities-from-extreme-weather/
via https://techempiresolutions.com/

from Paxton Willson https://paxtowillson.wordpress.com/2024/02/16/how-neara-uses-artificial-intelligence-to-protect-utilities-from-extreme-weather/
via https://techempiresolutions.com/