AI 中文总结
本文针对现有HVAC控制方法泛化性差的问题，提出物理感知扩散世界模型ADAPT，在IID控制下可降HVAC能耗7.3%、不适度30.2%，OOD场景下迁移鲁棒性显著优于现有方法。
AI 中文摘要
建筑物约占全球能源消耗和二氧化碳排放量的三分之一，优化室内气候系统对实现联合国可持续发展目标11（可持续城市和社区）与13（气候行动下的城市气候减缓至关重要。然而，室内延迟热力响应与部分可观测性严重阻碍了现有方法，这些方法主要受限于隐含热惯性、占用动态预测及累积预测误差，尤其在分布外（OOD）环境中表现更差。实际应用中，密集室内传感的高成本与隐私负担进一步加剧了这些挑战，迫使运营者仅在单一运行 regime 中收集有限数据，却期望控制器能在未见过的季节和气候区域可靠泛化。为解决该问题，本文提出ADAPT——一种面向HVAC控制的物理感知条件扩散室内环境世界模型。该模型预测短 horizon 的保持动作热力基线，以捕捉建筑物的潜在热惯性；扩散主干利用生成模型的鲁棒性，同时可学习的多区域热平衡正则化器约束生成轨迹满足可迁移建筑热力学，无需已知建筑几何或手动校准的热参数；随后为下游强化学习设计了信用分配机制。在SemibuildingSim和Sinergym上开展的大量实验表明，在独立同分布（IID）控制下，ADAPT相较于现有最优基线方法，将HVAC能耗降低7.3%，居住者不适度降低30.2%；在涵盖未见过季节与气候区域的OOD控制场景下，ADAPT仅出现轻微性能下降，仍保持鲁棒表现，在迁移鲁棒性上显著优于现有方法。
英文摘要
Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions. Optimizing indoor climate systems plays a critical role for urban climate mitigation aligned with UN Sustainable Development Goals 11 and 13. However, indoor delayed thermodynamic responses and partial observability severely hinder existing methods, which are primarily limited by implicit thermal inertia, occupancy dynamic prediction, and cumulative prediction errors, especially for out-of-distribution environments. In practice, these challenges are further exacerbated by the high cost and privacy burden of dense indoor sensing, forcing operators to collect only limited data in a single operating regime while expecting controllers to generalize reliably across unseen seasons and climate regions. To address this problem, we propose ADAPT, a physics-aware conditional diffusion indoor environmental world model for HVAC control. The model predicts a short-horizon held-action thermal baseline to capture the latent thermal inertia of the buildings. The diffusion backbone utilizes the robustness of generative models, while a learnable multi-zone heat-balance regularizer constrains generated trajectories to satisfy transferable building thermodynamics without requiring known building geometry or manually calibrated thermal parameters. A credit assignment is then design for the downstream reinforcement learning. Extensive experiments on SemibuildingSim and Sinergym demonstrate that ADAPT reduces HVAC energy consumption by 7.3\% and occupant discomfort by 30.2\% compared with state-of-the-art baselines under IID control. Under OOD control scenarios spanning unseen seasons and climate regions, ADAPT maintains robust performance with only marginal degradation relative to its IID performance, substantially outperforming existing methods in transfer robustness.
