Myst AI claims its AI energy prediction technology boosts utilities’ reliability
Myst AI, a startup developing a predictive energy usage platform, today closed a $6 million funding round. The proceeds will be used to expand Myst’s “forecasting as a service” offering as it seeks to acquire new customers, a spokesperson told VentureBeat via email.
The pandemic has prompted governments to institute shelter-in-place orders, quarantine mandates, and business closures. As millions continue to find themselves confined at home, the shift is straining not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation’s electrical grid. Load forecasting like Myst’s could ensure operations aren’t interrupted in the coming months, thereby preventing blackouts and brownouts while also bolstering the efficiency of utilities’ internal processes after the pandemic ends.
Myst claims to combine AI techniques with “highly localized” time-series data from a range of vendors, improving forecasting accuracy by as much as 30% to 60%. The company says this translates to “millions of dollars” in value for its customers; optionally, using the Myst platform, companies can build and deploy their own state-of-the-art forecasting models.
Myst proposes several use cases for its near-term energy forecasting technology, including electricity demand, renewable production, and market prices. The intention is to help renewable power investors increase revenues from solar and wind assets by optimizing wholesale market scheduling. On the load-serving entities side, Myst claims it helps utilities and community choice agencies lower energy costs and reduce carbon emissions.
By adjusting positions in power markets based on forecasts, Myst proposes, load-serving entities could save up to 1% in procurement costs while renewable generates increase profits by more than 5%.
Putting machine learning systems in charge of energy forecasting isn’t an outrageous notion. In 2016, Google implemented a system developed by DeepMind that provided energy recommendations to human datacenter overseers. In the company’s tests, it achieved a 40% reduction in the amount of energy used for cooling and a 15% reduction in overall power usage effectiveness — the ratio of the total building’s energy usage to its IT energy usage. Carbon Relay, a Foxconn-backed Boston and Washington, D.C.-based company, claims to accomplish much the same thing via a suite that leverages data collected by thousands of sensors to make predictions about datacenters’ electrical usage.
Beyond the profitability angle, there’s an urgent environmental need for systems like Myst’s — assuming they work as well as promised. It’s accepted science that carbon dioxide emissions contribute to climate change. CO2 molecules trap heat in the atmosphere, and they stick around for decades — 40% will remain for 100 years and 20% for 1,000 years. If wood, coal, natural gas, oil, and gasoline consumption remain on their current trajectory, the global temperature will rise between 2.5 and 10 degrees Fahrenheit over the next century, according to the Intergovernmental Panel on Climate Change (IPCC).
Myst, whose founders hail from Google and Nest among others, says it’s already working with a dozen energy companies in North America and Europe including Fortum, Enel Green Power, and East Bay Community Energy. The company has competition in Urbint, a startup developing AI-powered solutions for infrastructure and utility safety; Innowatts, which provides U.S. utilities an automated toolkit for energy monitoring and management; and Autogrid, which works with over 50 customers in 10 countries to deliver AI-informed power usage insights.
But San Francisco-based Myst’s solution was evidently robust enough to win over investors like Valo Ventures, which led the series A round. Google’s Gradient Ventures also participated.
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