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# 2024 GEMSE4 Smart Grids |
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GEMSE addresses the challenge of maintaining power grid stability amidst the increasing variability introduced by renewable energy sources. Traditional state estimation methods lack the speed and accuracy needed for real-time applications. By utilising an AI-driven, graph-based model, this solution integrates sensor data across subsystems, improving accuracy and reducing computational time to milliseconds. Successfully tested in a Swiss hydro-power plant, this approach enhances grid reliability, scalability, and promotes sustainability in renewable energy management
Raffael Theiler is a dedicated PhD student at the IMOS lab at EPFL. He holds a Master’s degree in "Neural Systems and Computation" from ETH and has a strong background in Neuroinformatics. Raffael’s research centers on the development of advanced AI-driven predictive algorithms, with a focus on applications in power generation and railway systems. His current work involves Transformer models for load forecasting and Graph Neural Networks to enhance short-term forecasting of pumped-storage hydroelectricity. Raffael is committed to translating his research into practical solutions, with the aim of founding a startup focused on state estimation technology. more
King Abdullah University of Science and Technology (KAUST)
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