Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations

TL;DR

This paper introduces six innovative physics-informed machine learning models that improve the accuracy and efficiency of indoor air quality predictions using sensor data, integrating domain physics with advanced ML techniques.

Contribution

The study presents novel physics-based ML architectures combining state-space models, GRUs, and decomposition, outperforming existing transformer-based models in indoor air quality approximation.

Findings

Models are less complex and computationally efficient.

Proposed models outperform state-of-the-art transformer models.

Models effectively capture nonlinear patterns in contaminated sensor data.

Abstract

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.