Ground-Truth-Aware Stress Testing of a Closed-Loop Smart-Building Digital Twin Under Sensor Drift and Missing Data

Abstract

Digital twins are increasingly used for smart-building monitoring and control, yet many evaluations focus on state estimation or fault detection rather than whether sensing errors materially affect closed-loop outcomes. This paper introduces a ground-truth-aware simulation framework that separates the latent physical state from a corrupted sensing layer and compares practical sensor-driven policies with an oracle controller. The synthetic twin includes 20 zones simulated at 15-minute intervals over 30 days and models occupancy-driven CO2, ventilation-energy trade-offs, sensor drift, measurement noise, and missing observations. Policy comparisons use a one-step information delay and common random numbers for paired Monte Carlo evaluation. Under nominal sensing, the raw-sensor controller disagreed with the oracle on 2.59% of decision steps, while aggregate CO2, energy, and comfort outcomes remained nearly unchanged. At 8x nominal drift, decision mismatch increased to 7.00%, but outcome gaps remained small. Across a 4x4 drift-missingness grid, none of 48 sensor-driven policy-condition combinations crossed the predeclared material-divergence thresholds. A three-sample rolling median increased nominal mismatch from 2.59% to 6.31% without meaningful outcome improvement, and Ground-Truth Regret rankings varied with utility weights. The results show that sensor error, decision disagreement, and outcome degradation are related but distinct. Because the study is fully synthetic and uncalibrated, its contribution is methodological rather than a claim of real-building performance.