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.
Journal of Building Performance Simulation 14, 586–610
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Constrained Bayesian optimization auto-tunes a building MPC controller, yielding 26.9% electricity cost reduction over rule-based control in a case study.
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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.
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What price to pay? Auto-tuning a building MPC controller for optimal economic cost
Constrained Bayesian optimization auto-tunes a building MPC controller, yielding 26.9% electricity cost reduction over rule-based control in a case study.