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arXiv Machine Learning By Fabian Raisch, Felix Koch, Zack Xuereb Conti, Christoph Goebel, Benjamin Tischler

Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models

Read the original on arXiv Machine Learning →

The paper presents a zero‑shot model predictive control (MPC) approach for buildings that uses excitation‑based generalized transfer learning models. By pretraining on purposefully probed operational data from multiple source buildings, the authors demonstrate that these models achieve superior control performance in 32 simulated target buildings, outperforming both an online linear MPC and a PI controller. This method eliminates the need for target‑specific data, reducing setup cost and facilitating broader deployment of energy‑efficient MPC in the building sector.

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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