AI 中文总结
本文提出真值感知仿真框架，针对传感器漂移与缺失数据场景测试智能建筑数字孪生的闭环控制，发现传感器误差、决策不一致与结果退化相关但有区别，贡献为方法论层面。
AI 中文摘要
数字孪生正越来越多地用于智能建筑的监控与控制，但许多评估聚焦于状态估计或故障检测，而非传感误差是否会对闭环结果产生实质性影响。本文提出一种真值感知仿真框架，该框架将潜在物理状态与受损坏的传感层分离，并将实际传感器驱动的策略与理想控制器（oracle controller）进行比较。合成数字孪生包含20个区域，以15分钟为时间步长模拟30天，模拟内容包括人员活动驱动的二氧化碳（CO₂）变化、通风-能耗权衡、传感器漂移、测量噪声及缺失观测。策略比较采用一步信息延迟和公共随机数进行配对蒙特卡洛评估。在标称传感条件下，原始传感器控制器与理想控制器在2.59%的决策步骤上意见不一致，而二氧化碳、能耗与舒适度的总体结果几乎未发生变化；在8倍标称漂移条件下，决策不匹配率升至7.00%，但结果差距仍较小。在4×4的漂移-缺失度网格中，48种传感器驱动策略-条件组合均未超过预先声明的实质性差异阈值。三样本滚动中位数将标称不匹配率从2.59%提升至6.31%，却未带来有意义的结果改善，且真值遗憾（Ground-Truth Regret）排名随效用权重而变化。结果表明，传感器误差、决策不一致与结果退化存在关联但相互区分。由于本研究完全基于合成数据且未经过校准，其贡献在于方法论层面，而非对实际建筑性能的断言。
英文摘要
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.
