Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations. Undoubtedly, accurate indoor air quality approximation significantly helps provide a healthy indoor environment, optimize associated energy consumption, and offer human comfort. However, it is crucial to design an ML architecture to capture the domain knowledge, so-called problem physics. In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations. The proposed models include an adroit combination of state-space concepts in physics, Gated Recurrent Units, and Decomposition techniques. The proposed models were illustrated using data collected from five offices in a commercial building in California. The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models. The superiority of the proposed models is due to their relatively light architecture (computational efficiency) and, more importantly, their ability to capture the underlying highly nonlinear patterns embedded in the often contaminated sensor-collected indoor air quality temporal data.

翻译：成本效益型传感器能够实时捕获各种空气质量相关模态，涵盖从不同污染物浓度到室内外湿度及温度数据。机器学习模型可执行空气质量的"超前"近似。毋庸置疑，精确的室内空气质量近似能显著促进健康室内环境的营造、优化相关能耗并提升人体舒适度。然而，设计能够捕捉领域知识（即问题物理机制）的机器学习架构至关重要。本研究提出六种基于物理的新型机器学习模型，用于精确近似室内污染物浓度。所提模型巧妙融合了物理学中的状态空间概念、门控循环单元与分解技术。我们利用加州某商业建筑内五间办公室采集的数据验证了模型性能。研究表明，所提模型与当前最先进的基于Transformer的模型相比，具有结构更简单、计算效率更高、精度更优的特点。其优越性源于相对轻量的架构（计算高效性），更关键的是能有效捕捉经常受污染传感器采集的室内空气质量时序数据中蕴含的高度非线性内在模式。