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arXiv Machine Learning By Fabian Raisch, Timo Germann, Sang-Woo Ham, J. Nathan Kutz, Christoph Goebel, Benjamin Tischler

Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control

Read the original on arXiv Machine Learning →

The paper introduces the Estimator from Scratch, a neural network that embeds RC thermal building equations to estimate parameters, and its extension, the Pretrained Estimator, which is pretrained on multiple buildings to avoid initial‑guess dependence. Both methods outperform a genetic‑algorithm estimator and a fully black‑box neural network across simulated and real buildings, achieving superior prediction accuracy and lower, more consistent MPC costs. The Pretrained Estimator is highlighted as a robust, computationally efficient, and initial‑guess‑free alternative for RC parameter estimation, with potential applicability to other control‑oriented dynamical systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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