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arXiv · 2303.09437

Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters

Yingzhao Lian·Jicheng Shi·Colin N. Jones
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Abstract

(Extended Version) 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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Yingzhao Lian, Jicheng Shi, Colin N. Jones. 2023-03-16. Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters. https://doi.org/10.1109/lcsys.2023.3282987

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