Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control
Gray-box RC models are widely used to enable energy-efficient model predictive control (MPC) in buildings. However, estimating RC parameters remains difficult, as conventional optimization-based algorithms are prone to local minima, rely heavily on good initial guesses, and incur high computational cost. To address these issues, we propose the Estimator from Scratch, a novel neural parameter estimation approach that embeds the physical equations into a neural network's training process to estimate RC parameters. To further improve estimation accuracy and eliminate dependence on an initial guess, we extend this approach by pretraining the neural network on data from multiple source buildin...
| Authors | Fabian Raisch, Timo Germann, Sang-Woo Ham et al. |
|---|---|
| Primary category | eess.SY |
| Published | 7 April 2026 |
| Last updated at source | 25 September 2026 |
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