# BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control

arXiv: https://arxiv.org/abs/2605.29849 · HTML: https://mlsanity.com/paper/2605.29849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research](https://mlsanity.com/paper/2512.00483/) (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)
- [Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control](https://mlsanity.com/paper/2604.05904/) (2026)

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

- [Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models](https://mlsanity.com/paper/2609.12853/) (2026)

## Abstract (arXiv)

Machine learning (ML) is increasingly used for data-driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and energy-efficient control. While recent work improves generalization across building characteristics, weather, and occupancy, generalization also depends on sufficient exploration of the control-driven system state space. Existing real-world datasets and simulation environments predominantly reflect stationary operation under fixed control policies, resulting in limited excitation and reduced robustness to unseen operating conditions. This paper introduces BuilDyn, a package based on BuilDa that enables customizable excitation strategies for control-oriented data generation. BuilDyn further supports sampling from representative building distributions and provides a Python interface for easy integration into machine learning pipelines. We demonstrate the benefits of BuilDyn by comparing the performance of data-driven ML models trained on non-excited and excited data for one building. With BuilDyn, we hope to advance scalable control-oriented modeling and support future directions such as transfer learning and building-specific foundation models.