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Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters. (arXiv:2303.09437v1 [eess.SY])

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Data-driven control can facilitate the rapid development of controllers,
offering an alternative to conventional approaches. In order to maintain
consistency between any known underlying physical laws and a data-driven
decision-making process, preprocessing of raw data is necessary to account for
measurement noise and any inconsistencies it may introduce. In this paper, we
present a physics-based filter to achieve this and demonstrate its
effectiveness through practical applications, using real-world datasets
collected in a building on the Ecole Polytechnique Federale de Lausanne (EPFL)
campus. Two distinct use cases are explored: indoor temperature control and
demand response bidding.

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