(12e) Efficient Training of Machine-Learning Enhanced Building Models with Noisy Data
AIChE Annual Meeting
Sunday, November 13, 2022 - 4:46pm to 5:05pm
A few approaches have been proposed for addressing the coupled estimation and identification problem for buildings (e.g., , , ). For example, an output integrating disturbance model was included when identifying the parameters of a physics-based building thermal dynamic model . A simultaneous model parameter and disturbance estimation procedure was proposed . Both modeling approaches result in a building thermal dynamic model and a model or estimator for estimating the unmeasured time-varying heat disturbance. However, the modeling goal for MPC is to obtain a model for predicting the building thermal dynamics over time. Accurately forecasting the time-varying heat disturbance is critical for achieving adequate closed-loop performance under MPC. Thus, as an alternative, a hybrid modeling framework consisting of a simplified physics-based model used to capture the indoor air and building mass thermal dynamics and a machine learning model used to capture the time-varying heat disturbances was proposed . The two underlying models that make up the hybrid model were trained with a simultaneous approach. With noise-free data, the method accurately identified the physics-based model parameters and a machine learning model that could forecast the heat disturbance. A preliminary training strategy was proposed for performing model training.
This work considers the training computational efficiency associated with hybrid model parameter identification using noisy data for buildings. The proposed hybrid model consists of a physics-based model describing the building's thermal dynamics and a neural network model that can forecast the building's unmeasured time-varying heat gains. In the proposed hybrid model, the parameters of the underlying physics-based model and machine learning-based models are trained simultaneously to prevent biased estimates of the physics-based parameters caused by the unmeasured heat gains. The training strategy is formalized, and improvements that address computational efficiency are investigated. To this end, a hybrid training strategy involving gradient descent with BFGS methods is proposed to solve the parameter estimation problem. Parameter regularization and training drop-out are considered to address noisy data. Considering data collected from a building zone, model training results employing the various training strategies proposed in this work are used to evaluate the accuracy of the resulting models and computational efficiency.
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