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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
Mengjie Zhao is a passionate Ph.D. researcher at the IMOS lab, EPFL, specializing in developing graph-based methods to model and understand the complex dynamics of industrial systems. With a solid foundation in computational mechanics, she is driven by a commitment to solving real-world challenges that remain at the frontier of research and application. Beyond academia, Mengjie is an active contributor to the Swiss startup ecosystem. Her entrepreneurial spirit fuels her ambition to transform cutting-edge research into innovative products that address critical industry needs.more
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King Abdullah University of Science and Technology (KAUST)
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