Eco Wave Power announced that it is in advanced discussions with Florida Atlantic University (FAU) and the University of Michigan to develop AI-powered wave energy applications, including the WaveGPT platform and a wave-powered coastal data center concept, aimed at supporting growing artificial intelligence energy demands.
The discussion took place during FAU’s strategy meeting, which included experts in marine renewable energy, artificial intelligence, digital twins, electrical engineering, ocean engineering, and energy systems optimization.
This effort focuses on two areas. The first is the continued development of WaveGPT, Eco Wave Power’s AI-driven operational intelligence platform. It is designed to analyze real-time operational data from the company’s wave energy facilities through predictive analytics, forecasting, anomaly detection, performance optimization, and digital twin technology.
As part of this effort, Eco Wave Power recently submitted a TEAMER application to FAU focused on data-driven energy flow mapping, operational intelligence, and predictive analytics for wave energy technology. This project aims to use AI techniques to improve system performance, operational planning, and future commercial deployment.
The second area includes a potential grant application by Eco Wave Power, FAU, and the University of Michigan to develop a wave-powered, AI-optimized coastal data center concept. The proposal combines wave energy generation, energy storage, advanced cooling technologies, digital twins, and intelligent workload management within a single platform designed for future coastal AI and edge computing infrastructure.
According to Eco Wave Power, the concept is based on the expectation that future data centers will require more power delivery, along with advanced energy management and cooling systems. The company said coastal regions may have opportunities to integrate renewable energy generation and seawater cooling technology.
The proposed platform uses AI-driven digital twins to predict wave conditions, computing workloads, cooling requirements, storage availability, and grid conditions in real-time to optimize operations across energy, water, and computing systems.
Discussion participants included Dr. Yufei Tang, director of FAU’s Florida Power & Light Center for Intelligent Energy Technologies (InETech); Dr. James Vanzwieten, Director of FAU’s Southeast National Marine Renewable Energy Center (SNMREC); Gabriel Alsenas, Associate Director of SNMREC and InETech. Dr. Sasha Fung, FAU Postdoctoral Researcher. Professor Lei Zuo of the University of Michigan. Louis King. And Weiyin Wong.
“Artificial intelligence is transforming the way energy systems are designed, monitored, and optimized,” said Dr. Yufei Tan, director of the FPL Intelligent Energy Technology Center (InETech) at Florida Atlantic University. “Combining ocean renewable energy, advanced digital twins, predictive analytics, and intelligent control systems provides an opportunity to develop next-generation energy infrastructures that are sustainable and adaptable to rapidly evolving energy demands. We are excited to work with Eco Wave Power and our academic partners to explore these research opportunities.”
Inna Braverman, Founder and CEO of Eco Wave Power, said: “AI is expected to be one of the biggest drivers of electricity demand over the next decade. We believe that wave energy can play a meaningful role in supporting the next generation of coastal digital infrastructure. By combining wave energy, AI optimization, advanced cooling technologies, and digital twins, renewable energy will become an integral part of AI-driven data centers and the edge. “We are looking for ways to directly support the rapidly expanding needs of computing facilities.”
Eco Wave Power said the proposed efforts form part of a broader strategy to integrate artificial intelligence, predictive analytics, digital twins and renewable energy generation into future energy infrastructure.
The company said consultation and grant applications remain subject to review, funding approval and final agreement between the parties.
