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AI software predicts how well an investment in charging will perform
Spotted: Many companies entering the electric vehicle (EV) charging industry face a challenge: they lack a robust analytics foundation for crucial investment decisions. Investing wisely demands information, yet the opacity of electricity tariffs has left EV charging businesses making uncertain gambles.
That’s where Stable comes in. This San Francisco-based company utilises a machine learning model to provide the essential missing information. By assessing portfolios of potential charging locations, forecasting returns, and delivering comprehensive return on investment (ROI) estimates, Stable aims to bring clarity to EV charging investments.
To do this, Stable analyses real data from thousands of charging stations, considering factors like traffic levels, nearby amenities, demographics, EV penetration rate, incentives, and more. Then, the company works backwards to figure out what the utilisation rate of a new site would have been if it had existed for the last year, helping Stable’s software to generate ten-year forecast scenarios.
“Stable’s Machine Learning models help financial institutions, infrastructure developers and charging networks turn arbitrary decisions into data-driven ones based on a variety of factors like average trip distance, nearby amenities and energy costs,” explains Stable co-founder and CEO Rohan Puri. “Predicting charger usage is just the beginning of EV adoption, and we’ll continue to help our customers deploy, track and monitor their investments.”
Stable has employed its prediction software to analyse 70 million data points for potential charging sites throughout the United States. The company recently concluded its series A funding round, spearheaded by Congruent Ventures, reaching a total funding milestone of $14 million (around €12.9 million).
Springwise has previously spotted other innovations that aim to speed up the transition to EVs, from a 15-minute charger to a 10-minute charge EV battery.
Written By: Georgia King