AI Tool Aims to Simplify Power Grid Management with More Accurate Forecasts

Researchers at the FAMU-FSU College of Engineering and Florida State University’s Center for Advanced Power Systems have developed an artificial intelligence tool that could make electric grids more reliable and cut operating costs. The system improves forecasts of electricity demand and renewable energy generation, giving grid operators better information to balance supply and demand.

As more renewable energy comes online, power grids are becoming increasingly difficult to manage. Forecast uncertainty can force operators to keep too much reserve power, increasing costs, or too little, raising the risk of blackouts. The FSU-developed model addresses this challenge by treating the power grid as a connected network to generate more accurate forecasts.

The research was published in IEEE Transactions on Network Science and Engineering. GridFusionX combines information such as past electricity demand, power generated from renewable sources, energy market prices, and other data points to generate predictions and confidence intervals. This helps operators run the grid more efficiently with less wasted reserve power.

A key innovation in this AI tool is its ability to use many different sources of information when making estimates. GridFusionX provides both spatially connected predictions and measures of uncertainty, addressing a critical gap in existing methods. This multi-modality allows for more accurate forecasts and better decision-making by grid operators.

In real-world tests across ten European regions, GridFusionX improved forecasting accuracy by up to 56% and cut reserve costs by as much as 66%, all while maintaining reliable service. The system uses a graph neural network to map how events in one region of the power grid can affect neighboring areas.

Grid operators will be able to plan reserves more efficiently and respond proactively to sudden changes, such as spikes in demand or drops in renewable generation. This could lead to significant cost savings for consumers who often pay too much due to overestimates of their energy usage.

The researchers’ approach treats the power grid as a dynamic puzzle: every factor, from different energy sources to shifting demands in cities and changing prices, must be considered. By analyzing these pieces continuously, they can predict energy needs more accurately and adapt to ensure both reliability and cost savings.

According to Associate Professor Olugbenga Moses Anubi, the system’s precision allows grid operators to plan reserves more efficiently and respond proactively to sudden changes. ‘Reducing uncertainty in our predictions lets us make smarter decisions that benefit daily life,’ he said.

The researchers are now incorporating concepts from this research into classes about cyber security systems for electric grids and artificial intelligence for power systems. Doctoral student researcher Quoc Bao Phan led the study, which was supported by the FAMU-FSU College of Engineering.

Assistant Professor Abdulrahman Takiddin also contributed to this study as a co-author. The work has significant implications for utility companies who often estimate energy usage to ensure consumers don’t underpay but end up overpaying as a result. With GridFusionX, predictions become more precise so bills match what customers truly use.

The vision behind the research is engineering intelligence: helping utility operators balance generation and load while reducing costs for consumers. The researchers aim to make energy usage more predictable and manageable, which could lead to significant benefits for both grid operators and consumers.

GridFusionX has already demonstrated its potential in real-world tests. By improving forecasting accuracy and cutting reserve costs, this AI tool can help power grids operate more efficiently and reduce waste. As the demand for renewable energy continues to grow, tools like GridFusionX will become increasingly important.