BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
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...
| Authors | Felix Koch, Thomas Krug, Fabian Raisch et al. |
|---|---|
| Primary category | eess.SY |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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