论文标题
物理受限的多区域建筑热力动力学的深度学习
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
论文作者
论文摘要
我们提出了一种面向控制的深度学习方法，用于建模建筑热动力学。所提出的方法基于将基于物理学的先验知识系统编码为结构化的复发性神经结构。具体而言，我们的方法将基于传统物理的建筑物建模的结构先验纳入神经网络热力学模型结构中。此外，我们利用惩罚方法来提供不平等约束，从而在身体上现实且安全的操作范围内进行界定预测。观察稳定特征值准确地表征了系统的耗散性，我们还基于Perron-Frobenius定理使用受约束的矩阵参数化来绑定构建热模型参数矩阵的主要特征值。我们证明了提议的数据驱动建模方法在带有20个热区的现实世界办公楼获得的数据集上的有效性和物理解释性。我们仅使用10天的训练测量值，我们证明了连续20天的概括，与文献中报道的先前最新结果相比，与先前的最新结果相比，准确性显着提高。
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.
