At Leaptran, we know that the future of the electric grid relies not just on generating clean power or even predicting it, but on utilizing intelligence to understand physical, statistical, and financial realities. Headquartered in San Antonio, Texas, Leaptran is an energy technology company specializing in high-fidelity forecasting, nowcasting, and predictive intelligence for distributed energy resources (DERs), solar generation, and large variable loads. Founded in energy management and evolved through forecasting and interconnection intelligence with artificial intelligence and machine learning platforms, Leaptran supports better decisions across renewable variability, grid constraints, and large-load integration. Our work helps utilities, developers, and energy-intensive customers improve visibility, readiness, and strategic confidence as the electric grid undergoes rapid transformation.
Our mission is to help utilities, ISOs/RTOs, and campus operators bridge the gap between research-grade forecasting and operational reliability. We help clients do more than adapt to change in the power system; we help them leap forward into the resilient and affordable energy future.
Leaptran launched in December 2016 as a spinoff from The University of Texas at San Antonio. Founded by tech entrepreneur Jeff Xu alongside university researchers, our initial focus was on smart cities and building management solutions.
Because commercial buildings consume roughly 70% of a city’s total electricity, our early vision was to optimize whole-building energy use and integrate local solar power with battery energy storage systems. Backed by an NSF I-Corps grant and a DOE SBIR Phase I award in 2017, we developed a proactive energy management system. This system combined proprietary software with integrated hardware to manage HVAC, solar, storage, and microgrid operations. Our early innovations in smart building operation and microgrid-level energy optimization even earned Leaptran an international Smart 50 Award in 2019.
While our early building energy controls were highly awarded, the reality of the startup landscape set in: developing and deploying both hardware and software simultaneously is an incredibly complex, capital-intensive effort. Through our work retrofitting buildings and deploying energy management systems, we made a crucial discovery. Our most valuable, scalable asset wasn't the physical hardware solutions or the building-level thermostats but rather the predictive algorithms we used to anticipate and optimize energy needs.
This realization drove our strategic pivot. We transitioned away from physical building management solutions to focus exclusively on our core strength: forecasting and nowcasting. By leveraging crowdsourced weather data, machine learning algorithms, and spatial-temporal modeling, we reinvented Leaptran into a software-driven data engine. Our expertise has since been commercially verified in addition to ranking in the top four in three separate DOE American-Made Solar and Net Load Forecasting competitions.
Today, Leaptran delivers ISO-grade forecasting tools that enhance grid visibility, ramp prediction, and market coordination. We have improved forecasting accuracy significantly over traditional methods. Our cloud-based platforms serve utilities navigating high DER penetration, AI data centers managing large variable loads, and merchant asset portfolios seeking a competitive advantage.
Leveraging our forecasting and nowcasting expertise, Leaptran has moved toward a fully-fledged grid intelligence solution. To achieve this goal, we partnered with Guadalupe Valley Electric Cooperative to apply for and win an award for the DOE’s Interconnection Innovation e-Xchange (i2X) program’s Innovative Queue Management Solutions (iQMS). As part of the i2X iQMS project, Leaptran is developing F-AST: the Forecasting-Accelerated Study Tool. This novel solution will accelerate DER generator interconnections at distribution voltage with eventual scaling to load interconnection and transmission voltage applications. F-AST can analyze a distribution utility’s grid in a consistent, affordable, and expedited manner to deliver a suite of metrics that provides enhanced visibility for more informed and efficient decision-making.
Read about our management team, with over 60 years combined experience in technology, machine learning and IT and infrastructure.
OUR LEADERSHIP
