PowerScout Secures $5.2 Million In Funding For Machine-Learning Clean Energy
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US-based clean energy adviser firm PowerScout announced earlier this month it had secured $5.2 million in funding for machine-learning technology that the company believes can lower the price of clean energy by predicting which homes are the most likely to adopt it.
Announced earlier this month, PowerScout revealed that it had secured $5.2 million in funding from a mix of private and public sources, which includes the US Department of Energy (DOE), which provided $2.5 million in grants as part of its SunShot Initiative. The move came only a few days after the SunShot Initiative itself received a promise of up to $107 million in funding from the US DOE for the express purpose of improving solar PV performance reliability and manufacturability, while also enabling greater solar market penetration.
PowerScout’s new funding will go toward machine-learning technology that it hopes will lower the price of clean energy by predicting which households and neighborhoods are most likely to adopt clean energy technologies.
The technology is known as Foresight, and uses a combination of advanced image recognition technology, LIDAR data collected by planes, and in-depth consumer data to analyze entire neighborhoods at once. Each home is thus tagged with over 1,200 data points ranging from income and education levels to political affiliation and even the resident type of car to predict the possibility of a future transition to clean energy. Foresight also analyses which houses already have solar or other clean technologies and makes further predictions about which other houses are likely to similarly adopt solar.
“We’re excited to bring the same technologies that are revolutionizing the retail, transportation, and financial services industries to the residential energy market,” said PowerScout CEO Attila Toth. “Today when you go solar through a traditional provider, you often end up paying more to cover marketing expenses than for the panels themselves. By pinpointing which homes are most likely to adopt solar we avoid wasteful spending and pass the savings on to the consumer.”
This type of machine-learning, which is always being updated with new information to increase its accuracy, is slowly being integrated into the future of renewable energy technologies. Machine-learning is different from ideas of artificial intelligence even though it inherently relies on computers making decisions independent of original programming. New information is inputted, and in line with past information and decisions, the computer is able to make predictions that can help in a number of situations. Foresight is using machine-learning to reduce marketing spending. GE Renewable Energy, for example, earlier this year announced it was using machine-learning to increase the efficiency of hydropower plants.
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