多区域建筑热动力学的物理约束深度学习
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
摘要：
我们提出了一种物理约束的面向控制的深度学习方法来建模建筑热动力学。所提出的方法是基于系统编码的物理为基础的先验知识到一个结构化的递归神经架构。具体来说，我们的方法将结构先验从传统的基于物理的建筑建模到神经网络热动力学模型结构。此外，我们利用惩罚方法来提供不等式约束，从而将预测限制在物理现实和安全的操作范围内。观察到稳定的特征值准确地表征了系统的耗散性，我们还使用了基于Perron-Frobenius定理的约束矩阵参数化来约束建筑物热模型参数矩阵的主导特征值。我们证明了所提出的数据驱动的建模方法的有效性和物理可解释性的数据集，从现实世界的办公楼与20个热区。仅使用10天的测量进行训练，我们证明了连续20天的泛化，与文献中报道的现有最先进的结果相比，显着提高了准确性。
英文摘要：
We present a physics-constrained control-oriented deep learning method for modeling building thermal dynamics. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our method incorporates structural priors from traditional physics-based building modeling into the neural network thermal dynamics model structure. Further, we leverage penalty methods to provide inequality constraints, thereby bounding predictions within physically realistic and safe operating ranges. Observing that stable eigenvalues accurately characterize the dissipativeness of the system, we additionally use a constrained matrix parameterization based on the Perron-Frobenius theorem to bound the dominant eigenvalues of the building thermal model parameter matrices. We demonstrate the proposed data-driven modeling approach's effectiveness and physical interpretability on a dataset obtained from a real-world office building with 20 thermal zones. Using only 10 days' measurements for training, we demonstrate generalization over 20 consecutive days, significantly improving the accuracy compared to prior state-of-the-art results reported in the literature.
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