BuilDyn supplies customizable excitation strategies and sampling tools to produce control-oriented datasets for machine learning models of building thermal dynamics.
Nouidui, and Xiufeng Pang
2 Pith papers cite this work. Polarity classification is still indexing.
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eess.SY 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Active learning for optimal experimental design in ML-based building energy system identification yields up to 54% lower RMSE than passive random sampling on the BOPTEST simulator across neural network and Gaussian process models.
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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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Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification
Active learning for optimal experimental design in ML-based building energy system identification yields up to 54% lower RMSE than passive random sampling on the BOPTEST simulator across neural network and Gaussian process models.