Bi Directional Feedback Fusion for Activity Aware Forecasting of Indoor CO2 and PM2.5

TL;DR

This paper introduces a novel bidirectional feedback fusion framework for indoor air quality forecasting, effectively modeling environmental and human activity influences to improve prediction accuracy and robustness.

Contribution

It proposes a dual stream bidirectional feedback architecture with context-aware modulation and dual timescale modules, advancing indoor pollutant forecasting methods.

Findings

Outperforms existing forecasting models on real-world datasets.

Provides interpretable uncertainty estimates for practical deployment.

Effectively captures both long-term and short-term pollutant dynamics.

Abstract

Indoor air quality (IAQ) forecasting plays a critical role in safeguarding occupant health, ensuring thermal comfort, and supporting intelligent building control. However, predicting future concentrations of key pollutants such as carbon dioxide (CO2) and fine particulate matter (PM2.5) remains challenging due to the complex interplay between environmental factors and highly dynamic occupant behaviours. Traditional data driven models primarily rely on historical sensor trajectories and often fail to anticipate behaviour induced emission spikes or rapid concentration shifts. To address these limitations, we present a dual stream bi directional feedback fusion framework that jointly models indoor environmental evolution and action derived embeddings representing human activities. The proposed architecture integrates a context aware modulation mechanism that adaptively scales and shifts each stream based on a shared, evolving fusion state, enabling the model to selectively emphasise behavioural cues or long term environmental trends. Furthermore, we introduce dual timescale temporal modules that independently capture gradual CO2 accumulation patterns and short term PM2.5 fluctuations. A composite loss function combining weighted mean squared error, spike aware penalties, and uncertainty regularisation facilitates robust learning under volatile indoor conditions. Extensive validation on real-world IAQ datasets demonstrates that our approach significantly outperforms state of the art forecasting baselines while providing interpretable uncertainty estimates essential for practical deployment in smart buildings and health-aware monitoring systems.