Building Indoor Environmental Data Reconstruction Under Alternate-Floor Sensor Deployment

This study addresses the challenge of incomplete indoor environmental monitoring data under alternate-floor sensor deployment in multi-story residential buildings. To enable cost-effective environmental sensing, a Building Environmental Data Reconstruction Framework (EDRF) is proposed for estimating unmonitored floor conditions. The EDRF integrates a convolutional neural network (CNN) for spatial feature extraction, a temporal convolutional network (TCN) for temporal dependency modeling, residual connections for stable feature propagation, and a multi-task learning (MTL) strategy for simultaneous reconstruction of multiple environmental variables. The model is trained and validated using real-world data collected from Floors 2–10 of a residential building, focusing on illuminance, temperature, and relative humidity. Experimental results demonstrate that the proposed framework achieves high reconstruction accuracy, with average R2 values of 0.992 for temperature and 0.988 for relative humidity. Even under a reduced sensor deployment rate of 33.3%, the model maintains robust performance with an overall R2 of 0.987. Ablation studies further confirm the effectiveness of each component in improving reconstruction accuracy. The proposed method provides a practical and scalable solution for reconstructing missing indoor environmental data and supports low-density sensor deployment in building monitoring systems.