Model predictive control enhances building energy performance; however, its reliability is highly dependent on the robustness of internal prediction models under severe operating conditions. To address this, a risk-aware model-then-control (RAMC) training framework is proposed in this study. This approach augments conventional prediction loss with a conditional value-at-risk (CVaR) penalty on operational costs under perturbed inputs, embedding tail-risk awareness directly into the prediction model. The framework is trained via standard backpropagation, avoiding the computational burden of differentiating through the controller. The proposed methodology is evaluated on a simulated commercial building equipped with a hydronic heating system under three weather scenarios. Compared to a standard fidelity-trained baseline, the strongest risk-aware configuration reduced occupied cold degree-hours by 22–26% and peak cold violations by 14–27%, demonstrating the greatest benefit under forecast bias. These comfort improvements were achieved alongside a 17–31% increase in weekly heating energy consumption. The results indicate that embedding tail-risk awareness into model training improves closed-loop comfort robustness relative to standard accuracy-based training. An ablation study attributes this improvement directly to the CVaR tail term, while the risk weight formalizes a tunable energy–comfort trade-off dictated by operational priorities. revtwogreenIn this case study, a fixed setpoint-margin baseline reached comparable cold protection at lower energy; the distinct contribution of RAMC is that it relocates a tunable tail-risk preference into the prediction model itself, leaving the downstream controller unchanged.
Monghasemi et al. (Sun,) studied this question.