Data-Driven Short-Term Forecasting of Indoor Thermal Fields via ST-JEPA for HVAC Feedforward Regulation

Abstract

Precise indoor thermal environment forecasting is critical for the energy-efficient operation and model predictive control of building heating, ventilation, and air-conditioning (HVAC) systems. However, conventional predictive models often struggle to untangle the complex spatiotemporal dynamics captured by sparse sensing networks. This study proposes a Spatio-Temporal Joint-Embedding Predictive Architecture (ST-JEPA) for indoor multi-node temperature and humidity prediction. By reformulating predictive representation learning from visual domains to structured sensor-node sequences, the proposed model explicitly integrates historical multi-node observations, spatial positional encoding, and concurrent HVAC operating states. The model was evaluated at prediction horizons from 30 s to 5 min using 3568 frames recorded over approximately 29.8 h by a nine-node sensor array in a single laboratory. Experimental results indicate that ST-JEPA achieves high short-term forecasting accuracy, with temperature root mean square errors (RMSE) of 0.0490 °C at 30 s and 0.0647 °C at 1 min. In the original ablation experiment, removing HVAC operating-state inputs increased temperature RMSE by up to 68.3%. Aggregate errors remained relatively stable under the tested 30–50% node-masking conditions, although additional single-node tests revealed location-dependent sensitivity. Comparisons with simpler baselines showed no consistent superiority across targets and horizons. ST-JEPA characterizes the short-term evolution of indoor thermal fields, providing a potential forecasting basis for HVAC feedforward regulation; its effects on energy consumption and thermal comfort remain to be evaluated.