A Self-Attention Fusion-Based BI-LSTM Framework for Occupant-Centric Prediction of Indoor Environmental Quality

The integration of occupant data into the management of indoor environment factors is gaining increasing attention for creating intelligent and inclusive built environments. Existing approaches have mostly relied on static models, often failing to account for the ever-changing nature of occupant behavior and environmental factors across time and dimensions. Recent advancements in deep learning, especially deep sequential models capable of capturing both local and global dependencies between time steps, provide an opportunity to overcome these challenges. To address these challenges, the authors propose an LSTM-based model framework that utilizes multimodal self-attention-based fusion, realtime occupant data, indoor environmental quality (IEQ) data, and outdoor environmental data to predict future IEQ conditions, preferred IEQ conditions, and classify current IEQ conditions based on collected occupant feedback. To develop and test the proposed framework, four key steps were followed: (1) collecting IEQ data through smart sensors, (2) collecting perceived occupant feedback, (3) collecting outdoor environmental data, and (4) developing an attention-fusion-based Bi-Directional LSTM(Bi-LSTM) model. The proposed framework was tested at the Virginia Tech Blacksburg campus, showing promising results.