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In recent decades, extreme weather events have not only become more severe, but they have also occurred more frequently. Close to a aims to allow utility companies and energy providers to create models of their power grids and anything that might affect them, like wildfires or floods. The Redfern, New South Wales, Australia-based startup recently launched AI and machine learning products that create large-scale network models and assess risks without having to conduct manual investigations .
Since its commercial launch in 2019, Neara has raised a total of AU$45 million (approximately US$29.3 million) from investors including Square Peg Capital, Skip Capital and Press Ventures. Its clients include Essential Energy, Endeavor Energy, SA Power Networks. It is also partnered with Southern California Edison Co and EMPACT Engineering.
Neara’s AI and machine learning-based capabilities are already part of its technology stack and have been 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 are spent on utility infrastructure, including maintenance, upgrades and labor costs. When something goes wrong, consumers are immediately affected. When Neara began integrating AI and machine learning capabilities into its platform, it was to analyze existing infrastructure without manual inspections, which it says can often be inefficient, inaccurate and costly.
Neara then expanded its AI and machine learning capabilities to be able to create a large-scale model of a utility’s network and environment. The models can be used in many ways, including simulating the impact of extreme weather on electricity supplies before, after and during an event. This can speed the restoration of power, keep utility crews safe, and mitigate the impact of weather events.
“The increasing frequency and severity of severe weather is driving our product development more than any single event,” says Curtis. “Recently, there has been an increase in severe weather events across the world and the network is impacted by this phenomenon. ” Some examples are the storm Ishawhich left tens of thousands of people without power in the UK, the winter storms which caused massive power outages across the United States and tropical cyclone storms in Australia which leave Queensland’s electricity network vulnerable.
Using AI and machine learning, Neara’s digital utility network models can prepare energy providers and utilities for this. Neara can predict certain situations, including when high winds could cause outages and wildfires, flood water levels that force grids to shut off their power, and accumulations of ice and snow that can make the less reliable and less resilient networks.
In terms of model training, Curtis says AI and machine learning were “built into the digital network from the start,” with LiDAR being critical to Neara’s ability to accurately simulate weather events. It adds that its AI and machine learning model was trained “across over a million miles of diverse network territory, helping us capture seemingly small but high-consequence nuances with hyper-precision.” “.
This is important because in scenarios like flooding, a difference of just one degree in elevation geometry can result in inaccurate water level modeling, meaning utilities might have to put power lines under voltage before you need it or, on the contrary, maintain the power supply longer than necessary. on.
Co-founders of Neara, Daniel Danilatos, Karamvir Singh and Jack Curtis
LiDAR images are captured by utility companies or third-party capture companies, instead of LiDAR. Some customers scan their networks to continually feed Neara new data, while others use them to gain new insights from historical data.
“One of the key outcomes of ingesting this LiDAR data is the creation of the digital twin model,” says Curtis. “That’s where the power lies, as opposed to raw LiDAR data.”
Some examples of Neara’s work include Southern California Edison, where his goal is “self-prescription,” or automatically identifying where vegetation is likely to catch fire, with more accuracy than manual surveys. It also helps inspectors direct investigation teams where to go, without putting them in danger. Since utility networks are often huge, different inspectors are sent to different areas, which means multiple sets of subjective data. Curtis says using the Neara platform helps maintain more consistent data.
In the case of Edison in Southern California, Neara uses LiDAR and satellite imagery and simulates the elements that contribute to wildfires spreading through vegetation, including wind speed and ambient temperature. But some things that make predicting vegetation hazards more complex are that Southern California Edison has to answer more than 100 questions for each of its utility poles due to regulations and is also required to inspect its system transmission each year.
In the second example, Neara began working with SA Power Networks in Australia after the 2022-2023 Murray River flood crisis, which affected thousands of homes and businesses and is considered one of the worst natural disasters to hit South Australia. SA Power Networks captured LiDAR data from the Murray River region and used Neara to carry out digital modeling of the impact of the flooding and see how much of its network was damaged and how much risk remained.
This enabled SA Power Networks to complete a report in 15 minutes analyzing 21,000 spans of power lines in the flooded area, a process that would otherwise have taken months. Thanks to this, SA Power Networks was able to re-energize the power lines in five days, compared to the three weeks initially planned.
3D modeling also allowed SA Power Networks to model the potential impact of different levels of flooding on parts of its electricity distribution networks and predict where and when power lines might breach permissions or pose a risk electrical disconnection. Once river levels returned to normal, SA Power Networks continued to use Neara’s modeling to help plan the reconnection of its power supply along the river.
Neara is currently doing more machine learning R&D. One goal is to help utilities derive more value from their existing current and historical data. It also plans to increase the number of data sources that can be used for modeling, with a focus on image recognition and photogrammetry.
The startup is also developing new features with Essential Energy that will help utilities assess every asset, including poles, on a network. Individual assets are currently evaluated based on two factors: the likelihood of an event such as extreme weather and their ability to withstand those conditions. Curtis says this type of risk/value analysis is typically done manually and sometimes doesn’t prevent outages, as in the case of power outages during the California wildfires. Essential Energy plans to use Neara to develop a digital grid model that will be able to perform more accurate asset analysis and reduce risk during wildfires.
“Essentially, we enable utilities to stay ahead of extreme weather by understanding exactly how it will affect their network, allowing them to keep the lights on and their communities safe,” says Curtis.
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