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.
IEEE Transactions on Power Electronics 36, 3744–3756
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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.