A physics-based domain adaptation framework for modeling and forecasting building energy systems

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-08-19 DOI:10.1017/dce.2023.8
Zack Xuereb Conti, R. Choudhary, L. Magri
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Abstract

Abstract State-of-the-art machine-learning-based models are a popular choice for modeling and forecasting energy behavior in buildings because given enough data, they are good at finding spatiotemporal patterns and structures even in scenarios where the complexity prohibits analytical descriptions. However, their architecture typically does not hold physical correspondence to mechanistic structures linked with governing physical phenomena. As a result, their ability to successfully generalize for unobserved timesteps depends on the representativeness of the dynamics underlying the observed system in the data, which is difficult to guarantee in real-world engineering problems such as control and energy management in digital twins. In response, we present a framework that combines lumped-parameter models in the form of linear time-invariant (LTI) state-space models (SSMs) with unsupervised reduced-order modeling in a subspace-based domain adaptation (SDA) approach, which is a type of transfer-learning (TL) technique. Traditionally, SDA is adopted for exploiting labeled data from one domain to predict in a different but related target domain for which labeled data is limited. We introduced a novel SDA approach where instead of labeled data, we leverage the geometric structure of the LTI SSM governed by well-known heat transfer ordinary differential equations to forecast for unobserved timesteps beyond available measurement data by geometrically aligning the physics-derived and data-derived embedded subspaces closer together. In this initial exploration, we evaluate the physics-based SDA framework on a demonstrative heat conduction scenario by varying the thermophysical properties of the source and target systems to demonstrate the transferability of mechanistic models from physics to observed measurement data.
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建筑能源系统建模和预测的基于物理的领域自适应框架
最先进的基于机器学习的模型是建模和预测建筑物能源行为的流行选择,因为给定足够的数据,它们善于发现时空模式和结构,即使在复杂性禁止分析描述的情况下。然而,它们的结构通常与控制物理现象的机械结构不具有物理对应关系。因此,它们成功推广未观察到的时间步长的能力取决于数据中观察系统的动态代表性,这在现实世界的工程问题中很难保证,例如数字孪生中的控制和能量管理。作为回应,我们提出了一个框架,该框架将线性时不变(LTI)状态空间模型(ssm)形式的集总参数模型与基于子空间的域适应(SDA)方法中的无监督降阶建模相结合,这是一种迁移学习(TL)技术。传统上,SDA是利用一个领域的标记数据来预测一个不同但相关的目标领域,而目标领域的标记数据是有限的。我们引入了一种新的SDA方法,在这种方法中,我们利用LTI SSM的几何结构(由众所周知的传热常微分方程控制)来预测超出可用测量数据的未观察到的时间步,方法是将物理衍生和数据衍生的嵌入子空间更紧密地排列在一起。在这一初步探索中,我们通过改变源系统和目标系统的热物理性质来评估基于物理的SDA框架,以验证从物理到观测测量数据的机制模型的可转移性。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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