FSU researchers build GridFusionX AI model to forecast power grids across regions
FAMU-FSU engineers built GridFusionX, an AI grid forecasting model that improved accuracy by up to 56% and cut reserve costs by up to 66% in tests.
Photo by Petr Ganaj on Pexels
Researchers at the FAMU-FSU College of Engineering and Florida State University’s Center for Advanced Power Systems published a new AI grid forecasting model called GridFusionX on August 3, 2026, designed to predict power system conditions across interconnected regions rather than one grid area at a time, according to an FSU News release. In tests across ten European regions, the model improved forecasting accuracy by up to 56 percent and cut reserve costs by as much as 66 percent compared with the researchers’ baseline methods, FSU reported.
The work, titled “GridFusionX: Network-Aware Probabilistic Forecasting for Multi-Regional Power Systems,” was published in IEEE Transactions on Network Science and Engineering. Doctoral student Quoc Bao Phan led the study, with co-authors Tuy Nguyen, Olugbenga Moses Anubi and Abdulrahman Takiddin, all faculty in FSU’s Department of Electrical and Computer Engineering, and associate professor Ravikumar Gelli, according to the university.
How the AI grid forecasting model works
GridFusionX combines multiple data streams, including past electricity demand, renewable generation output, energy market prices and grid network topology, using a graph neural network to model how conditions in one grid region affect neighboring regions connected through shared power lines, weather patterns and energy markets, FSU said. The model produces both forecasts and uncertainty ranges rather than single-point predictions, which the university said helps grid operators judge how much confidence to place in a given forecast.
That distinction matters as grids carry more variable renewable generation. Winss has reported that US renewables reached 30.3 percent of electricity generation in early 2026, a share that increases the forecasting challenge GridFusionX is built to address, since wind and solar output shift faster and less predictably than conventional generation.
| GridFusionX test result | Figure |
|---|---|
| Test regions | 10 European regions |
| Forecasting accuracy improvement | Up to 56% |
| Reserve cost reduction | Up to 66% |
| Publishing venue | IEEE Transactions on Network Science and Engineering |
| Lead researcher | Quoc Bao Phan, doctoral student |
| Funding | FAMU-FSU College of Engineering |
Nguyen described the aim of treating the grid as “a dynamic puzzle” in the FSU release. Anubi said the approach could also sharpen utility billing: “Right now, utility companies estimate your usage… With our approach, predictions become more precise, so your bill matches what you truly use,” he said. Gelli framed the broader goal as building “engineering intelligence” into grid operations. FSU’s release states the project was supported by the FAMU-FSU College of Engineering; it does not name an external funding agency or grant number.
FSU’s release does not specify the baseline forecasting method GridFusionX was measured against, and the underlying IEEE paper’s abstract page was not accessible to confirm additional methodological detail beyond what the university disclosed.
About FSU’s Center for Advanced Power Systems
The Center for Advanced Power Systems is a research unit within the FAMU-FSU College of Engineering, a jointly operated engineering school of Florida State University and Florida A&M University in Tallahassee, Florida. The center’s Cyber-Physical Systems Security Group, which produced GridFusionX, focuses on power systems research spanning grid security, forecasting and control. The August 2026 publication extends that work from single-region grid modeling toward forecasting that accounts for how neighboring grid regions influence each other, a direction the researchers say is increasingly relevant as more renewable generation and cross-border power flows make single-region forecasts less reliable on their own.
Sources: Florida State University News; TechXplore; IEEE Transactions on Network Science and Engineering
Featured image: photo by Petr Ganaj on Pexels (free Pexels license).
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I specialize in sustainability education, curriculum co-creation, and early-stage project strategy. At WINSS, I craft articles on sustainability, transformative AI, and related topics. When I’m not writing, you’ll find me chasing the perfect sushi roll, exploring cities around the globe, or unwinding with my dog Puffy — the world’s most loyal sidekick.
