Partially stochastic deep learning with uncertainty quantification for model predictive heating control

Improving the energy efficiency of building heating systems is crucial for reducing global energy consumption and greenhouse gas emissions. Traditional control methods rely on static heating curves that are based solely on outdoor temperature, neglecting system state measurements, such as indoor temperature, and free heat sources, such as solar gain. A more effective strategy is model predictive control (MPC), which optimizes heating control by incorporating system state predictions based on weather forecasts, among other factors. However, current industrial MPC solutions often employ simplified physics-inspired indoor temperature models, sacrificing accuracy for robustness and interpretability. To bridge this gap, we propose a partially stochastic deep learning (DL) architecture for building-specific indoor temperature modeling. Unlike most studies that evaluate model performance through simulations or limited test buildings, our experiments across a large dataset of 100 real-world buildings, covering various heating season conditions, demonstrate that the proposed model outperforms a widely used industrial physics-based model in predictive accuracy. The proposed DL architecture shows significant potential to improve thermal comfort and energy efficiency in heating MPC solutions. Although its computational cost is higher than that of the reference model, we discuss why this trade-off is manageable, even in large-scale applications. Unlike deterministic black-box approaches, the partially stochastic DL model offers a critical advantage by enabling pre-assessment of model feasibility through predictive uncertainty quantification. This work advances heating MPC, particularly for buildings with comprehensive datasets on their thermal behavior under various weather conditions.