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

arXiv: https://arxiv.org/abs/2609.12853 · HTML: https://mlsanity.com/paper/2609.12853/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC](https://mlsanity.com/paper/2110.15911/) (2021)
- [GenTL: A General Transfer Learning Model for Building Thermal Dynamics](https://mlsanity.com/paper/2501.13703/) (2025)
- [Modeling Unknown Stochastic Dynamical System Subject to External Excitation](https://mlsanity.com/paper/2406.15747/) (2024)
- [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)
- [NeurOpt: Neural network based optimization for building energy management and climate control](https://mlsanity.com/paper/2001.07831/) (2020)
- [BUILDA: A Thermal Building Data Generation Framework for Transfer Learning](https://mlsanity.com/paper/2508.12703/) (2025)
- [A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research](https://mlsanity.com/paper/2512.00483/) (2025)
- [Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review](https://mlsanity.com/paper/2503.05022/) (2025)
- [Thermal-GEMs: Generalized Models for Building Thermal Dynamics](https://mlsanity.com/paper/2604.16443/) (2026)
- [Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models](https://mlsanity.com/paper/2605.04555/) (2026)
- [Toward a foundational thermal model for residential buildings](https://mlsanity.com/paper/2605.01364/) (2026)
- [BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control](https://mlsanity.com/paper/2605.29849/) (2026)

## Abstract (arXiv)

The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for individual buildings. Transfer learning (TL) has consequently gained increasing attention for target building modeling, as it reduces data requirements and modeling effort by reusing pretrained source models. However, these TL models are typically evaluated only on prediction accuracy in the target, without testing downstream control performance. To address this gap, we apply a state-of-the-art TL approach - pretraining a generalized model on multiple source buildings using standard operational data - within an MPC setup in a target building. We show that this approach is insufficient to achieve satisfactory control performance. As a solution, we introduce generalized models pretrained on excitation-based operational source data - purposefully probed inputs that explore the building's state-action space. For evaluation, we apply the generalized models via zero-shot (i.e., without fine-tuning) to 32 simulated target buildings and assess MPC performance. Our results show that excitation-based generalized models achieve the strongest control performance among all benchmarks, outperforming an online linear model-based MPC and a PI controller by 6.4% and 36.9%, respectively. By combining strong control performance with the ability to generalize across multiple buildings, without requiring any target-specific data, our approach reduces MPC setup cost and simplifies its widespread deployment in the building sector.