BuilDyn supplies customizable excitation strategies and sampling tools to produce control-oriented datasets for machine learning models of building thermal dynamics.
A highly configurable framework for large-scale thermal building data generation to drive machine learning research
3 Pith papers cite this work. Polarity classification is still indexing.
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eess.SY 3years
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UNVERDICTED 3representative citing papers
Transfer learning with pretraining-fine-tuning improves neural parameter estimation accuracy for building RC models by 18.6-49.4% over baselines while removing the need for initial parameter guesses.
Multi-source transfer learning for building thermal dynamics yields up to 63% lower forecasting errors than single-source models and outperforms time series foundation models when pretrained on 16-32 buildings over one year.
citing papers explorer
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BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
BuilDyn supplies customizable excitation strategies and sampling tools to produce control-oriented datasets for machine learning models of building thermal dynamics.
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Transfer Learning for Neural Parameter Estimation applied to Building RC Models
Transfer learning with pretraining-fine-tuning improves neural parameter estimation accuracy for building RC models by 18.6-49.4% over baselines while removing the need for initial parameter guesses.
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Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Multi-source transfer learning for building thermal dynamics yields up to 63% lower forecasting errors than single-source models and outperforms time series foundation models when pretrained on 16-32 buildings over one year.