Non-stationarity refers to a situation where historical data may not always be a reliable predictor of what’s going to happen in the future. One of the key things that makes it different from other types of use cases in machine learning is the fact that you have to deal with this notion of non-stationarity.”
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The second realization that I had is that time series forecasting, as a problem, is very difficult to solve. It doesn’t matter that much if you’re trying to forecast electricity demand or it’s forecasting how many products a company needs to have in stock, the uses cases are basically infinite for time series forecasting.
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I had two big realizations on how to work on this problem. Now you’re trying to create a model that can predict into the future what the hourly electricity demand will be. “The problem is that you have some historical time series datasets, which could, for example, be electricity demand,” Verhoeven told pv magazine. This focus on time series data comes from Verhoeven’s time at Nest and Google, where he helped to develop Nest’s demand response program - Rush Hour Rewards.
“The reason why we chose to focus on electricity initially is because this is a place where accurate near-term forecasting is a critical component of the transition that we’re now in and will be in for the next ten or more years,” Myst co-founder Titiaan Palazzi said. Specifically, Myst helps electricity companies with near-term forecasts of electricity demand, renewable production and market prices. Myst creates precise forecasting models by combining artificial intelligence techniques with volumes of time series data from a large set of vendors, making it especially localized.
Having accurate forecasting is a really critical component of making sure that’s the case.” At the end of the day, a lot of this comes down to demand and supply and making sure they’re in balance. “This is one of the reasons why we feel extremely motivated to work. “We really strongly believe that many of the biggest problems that we’re facing today as humanity can be solved, in part, by better forecasting,” Myst co-founder and CEO, Pieter Verhoeven, told pv magazine. Myst AI, an artificial intelligence platform for time series forecasting in electricity, just closed on its $6 million Series A round led by Valo Ventures.Īccording to representatives at Myst, the funding will be used to expand the company’s Forecasting as a Service (FaaS) offering.įor the team at Myst, the goal has always been to mitigate the effects of climate change and find the most effective way to get a high volume of renewables not only on the grid, but generating and contributing to the grid in the most effective way possible.
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