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Abstract:

With the electrification of society and the rising concern about the energy system's impact on the environment, there is an increasing need to efficiently operate the energy networks. Data-driven control techniques seem a promising alternative to conventional control mechanisms for this partial observable control problem. Combining building physical models with data-driven control mechanisms allows the latter to be trained in a realistic environment before deployment. The goal of this work is to investigate how multi-carrier energy systems (electric power, heat, ...) and their distributed controllers can benefit from this combination, and how machine learning can improve controller performance.