实测SAC控制空调的最低能耗为每天35.51美元，接近理论极限。
Quantifying the Energy Floor: Direct Measurement and Replay Buffer Bias in SAC-Based HVAC Control on sbsim
- 通过最小动作实验直接测量能量下限，发现99.8%能耗来自电力。
- 初始缓冲区导致4.7%性能差距，清空缓冲区可消除96%偏差。
- 适合关注强化学习在建筑节能中实际瓶颈的研究者。
本文量化了软演员-评论家（SAC）算法在经过校准的sbsim建筑模拟器上进行暖通空调（HVAC）控制时的能量下限——即在动作空间约束下可达到的最低成本。通过最小动作实验，直接测得该下限为每日35.51美元，其中连续电耗占35.44美元（99.8%），燃气消耗可忽略。标准SAC基线（以调度策略初始化的重放缓冲区）收敛至37.18美元/天，比下限高出4.7%。我们识别出缓冲区初始化是此场景下次优性的主要来源：从空缓冲区开始训练可将成本降至35.57美元/天，消除96%的差距。将供水温度范围扩大10 K仅带来0.03美元/天的微小节省，进一步扩展则触发物理约束违规。此外，我们发现折扣因子耦合效应（有效γ=0.891）使有效规划时长从8.3小时缩短至46分钟，这一广泛存在的问题需系统性审计。系统性消融实验表明，所有预填充缓冲区配置的成本均集中在37.18至37.42美元之间（波动≤0.7%），证明设备最小功率才是决定性约束，而非算法设计。
原文摘要 · Abstract (English)
We quantify the energy floor -- the minimum achievable cost given action space constraints -- for Soft Actor-Critic (SAC) HVAC control on the sbsim calibrated building simulator. Through minimum-action experiments, we directly measure this floor at USD 35.51/day, dominated by continuous electrical loads (USD 35.44, 99.8%) with negligible gas consumption. The standard SAC baseline, initialized with schedule-policy replay buffer transitions, converges to USD 37.18/day, 4.7% above the floor. We identify buffer initialization as the dominant source of sub-optimality in this scenario: training from an empty buffer reduces cost to USD 35.57/day, eliminating 96% of the gap. Expanding the supply water temperature range by 10 K yields negligible additional savings (USD 0.03/day), and further expansion triggers physical constraint violations. We additionally uncover a discount factor coupling (gamma_eff = 0.891) shrinking the effective planning horizon from 8.3 h to 46 min -- a benchmark-wide issue warranting audit. Systematic ablation across planning horizon, reward weights, and observation enrichment confirms all pre-filled-buffer configurations cluster within 0.7% (USD 37.18--USD 37.42), demonstrating that equipment minimum power -- not algorithmic design -- imposes the binding constraint.
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