San Antonio Startup Leaptran Develops Machine-Learning Energy Tech
San Antonio—A pair of San Antonio researchers have created a startup that aims to reduce the cost of energy consumption for commercial buildings using solar power and machine learning technology.
Called Leaptran, the young startup plans to help commercial building owners, such as at a university or warehouse, use solar panels to soak up and store energy in batteries for potential future use. The company says its software can help the building owner predict how much power the building will use and when—based on data analytics of previous usage—which is especially useful when utilities face high levels of power consumption and charge higher rates. Leaptran’s co-founders believe the businesses or owners of the commercial buildings can then switch their power usage from the utility to the energy saved and stored in its batteries.
“We can predict and control when the battery will discharge to the building, instead of from the grid,” says Bing Dong, a co-founder and assistant professor at University of Texas at San Antonio, which licensed the technology to Leaptran.
The process is called distributed energy storage, says co-founder Jeff Xu, the company’s president and CEO. Xu was previously a research scientist at the Southwest Research Institute, where he focused on technical aspects of battery storage and development.
The two-year-old startup is still working through some of the details of how it will sell its product, such as whether it will use commercial solar panels and batteries or develop its own—in part because it is still testing everything out. Leaptran has received three grants worth a total of around $300,000, the most recent of which it is collaborating on with the University of Alabama to study sensor-based energy saving models. Leaptran is currently trying to raise a seed funding round of less than $1 million to develop a beta product.
Leaptran also may develop sensors that can act like a brain for commercial buildings, similar to smart-home products for residential use.
Xu says he thinks utilities will appreciate a product that helps limit energy consumption during peak hours, helping avoid blackouts and the need to buy energy from other utilities in extremely high demand situations.
“Utility companies have less control of demand,” Xu says. “Our technology would allow utilities to benefit from automated demand response.”