打开黑箱：利用建筑物理洞察构建具有内在可解释性的能耗数据填补模型
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight
AI总结：
本文提出物理信息去噪自编码器（PI-DAE），通过在去噪自编码器损失函数中引入建筑热平衡物理软约束，提升商业建筑缺失数据填补模型的内在可解释性和对缺失率的鲁棒性。
摘要：
在建筑能耗建模领域，从业者和研究人员经常遇到数据缺失问题。为此，通常需要采用先进的数据驱动方法（如深度学习方法）来反映这些异常的非线性行为。作为深度学习研究中一个持续存在的问题，可以通过在网络中引入先验知识来探索模型在有限数据场景下的适用性。同样的策略还可以带来更具可解释性的预测结果，从而促进该方法在实际现场中的应用。为此，本文旨在提出使用物理信息去噪自编码器（Physics-informed Denoising Autoencoders，PI-DAE）对商业建筑中的缺失数据进行填补。具体而言，所提出的方法在去噪自编码器（Denoising Autoencoder，DAE）的损失函数中施加了受物理启发的软约束。为了量化物理组件带来的收益，本文对不同DAE配置进行了消融研究。首先，分别针对室内空气温度、供暖和制冷数据优化了三个单变量DAE。然后，从之前的配置中派生出两个多变量DAE。最后，将建筑热平衡方程与最后一个多变量配置耦合，得到PI-DAE。此外，本文还采用了两种常用的基准方法来支持研究结论。结果表明，在多变量去噪自编码器中引入物理知识可以通过优化得到的基于物理的系数增强模型的内在可解释性。尽管所提出的PI-DAE在重构误差方面没有观察到显著改进，但其对不同缺失数据比例下更强的鲁棒性以及从基于物理的系数中获得的有价值见解，为建筑系统和建成环境中的更广泛应用创造了机会。
英文摘要：
Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.
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