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

2026-05-28

AI Summary

Existing building thermal dynamics datasets are predominantly derived from steady-state operation under fixed control policies, which inadequately excite the system’s state space and consequently limit the generalization capability of data-driven models. To address this, this work proposes BuilDyn, a novel toolkit that introduces, for the first time, a control-oriented active excitation mechanism. By combining sampling across building parameter distributions with customizable excitation strategies, BuilDyn generates thermal dynamics data with high state-space coverage and integrates seamlessly into machine learning workflows via a Python API. Experimental results demonstrate that models trained on BuilDyn-generated data significantly outperform those based on conventional datasets in both predictive accuracy and generalization, thereby establishing a robust data foundation for control-oriented modeling, transfer learning, and the development of building-specific foundation models.

Abstract

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.

Problem

• building thermal dynamics
• data-driven modeling
• excitation
• control-oriented data
• generalization

Innovation

• excitation-driven data generation
• building thermal dynamics
• control-oriented modeling
• data-driven machine learning
• system state space exploration