Data-driven energy solutions and AI

As in many other fields, more and more data are being collected and made available in relation to energy systems. Such data may be used for developing dedicated visualizations, hence allowing to raise awareness (e.g., related to availability of renewable energy, CO2 emissions, etc.), but also for optimizing operation of energy systems.

Data-driven energy solutions require the development of advanced mathematical methods that are able to deal with a large amount of data for both state estimation, prediction, optimization, etc. Today, a strong emphasis is placed on the role of machine learning and AI in the digitization of power and energy systems.

Our research focuses on the proposal and validation of such approaches, from methodological development to application and validation based on real-world cases. Here, we are making fundamental contributions to AI and data science, by focusing on interpretable machine learning (also with guarantees), advanced analytics in a market framework (e.g., towards data markets), collaborative analytics and online distributed learning, privacy-preserving analytics, etc. In view of the quantity of data to be handled and of the complexity of the algorithms involved, emphasis is placed on the computational aspects for parallelization and optimal usage of high-performance computing capabilities.

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