(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.
@article{arxiv.2303.09437,
title = {Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters},
author = {Yingzhao Lian and Jicheng Shi and Colin N. Jones},
journal= {arXiv preprint arXiv:2303.09437},
year = {2023}
}