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

arXiv: https://arxiv.org/abs/2604.05904 · HTML: https://mlsanity.com/paper/2604.05904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics](https://mlsanity.com/paper/2011.05987/) (2020)
- [Neural parameter calibration for large-scale multi-agent models](https://mlsanity.com/paper/2209.13565/) (2022)
- [GenTL: A General Transfer Learning Model for Building Thermal Dynamics](https://mlsanity.com/paper/2501.13703/) (2025)
- [Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts](https://mlsanity.com/paper/2508.21615/) (2025)
- [BUILDA: A Thermal Building Data Generation Framework for Transfer Learning](https://mlsanity.com/paper/2508.12703/) (2025)
- [State-Space Models for Tabular Prior-Data Fitted Networks](https://mlsanity.com/paper/2510.14573/) (2025)
- [A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research](https://mlsanity.com/paper/2512.00483/) (2025)
- [Online Model Estimation for Predictive Thermal Control of Buildings](https://mlsanity.com/paper/1601.02947/) (2015)
- [Building Model Identification during Regular Operation - Empirical Results and Challenges](https://mlsanity.com/paper/1603.06872/) (2016)
- [Hybrid modeling approach for better identification of building thermal network model and improved prediction](https://mlsanity.com/paper/2512.05400/) (2025)

## Built on by (most cited first, 1 of 1)

- [BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control](https://mlsanity.com/paper/2605.29849/) (2026)

## Abstract (arXiv)

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 buildings, the Pretrained Estimator. We benchmark both methods against a genetic-algorithm-based RC estimator and a fully black-box neural network. All methods are evaluated across eight simulated and three real-world buildings for two RC configurations, for both prediction accuracy and closed-loop MPC performance, the latter only for the simulated buildings. The Pretrained Estimator achieves the best prediction performance among all RC-based methods, particularly with little training data, and yields the lowest and least variable MPC costs across buildings and benchmarks. These results position the Pretrained Estimator as a robust, computationally efficient, and initial-guess-free alternative for RC parameter estimation, with potential to extend to other control-oriented dynamical systems.