Transfer Learning for Neural Parameter Estimation applied to Building RC Models
Abstract
Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate and fast parameter estimation but do not exploit transferable knowledge across systems. To address this, we introduce a transfer-learning-based neural parameter estimation framework based on a pretraining-fine-tuning paradigm. This approach improves accuracy and eliminates the need for an initial parameter guess. We apply this framework to building RC thermal models, evaluating it against a Genetic Algorithm and a from-scratch neural baseline across eight simulated buildings, one real-world building, two RC model configurations, and four training data lengths. Results demonstrate an 18.6-24.0% performance improvement with only 12 days of training data and up to 49.4% with 72 days. Beyond buildings, the proposed method represents a new paradigm for parameter estimation in dynamical systems.
Keywords
Cite
@article{arxiv.2604.05904,
title = {Transfer Learning for Neural Parameter Estimation applied to Building RC Models},
author = {Fabian Raisch and Timo Germann and J. Nathan Kutz and Christoph Goebel and Benjamin Tischler},
journal= {arXiv preprint arXiv:2604.05904},
year = {2026}
}
Comments
This work has been submitted to the IEEE for possible publication