用多样性进化强化学习优化热带商业建筑空调系统，提升能效与适应性。
Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings
- 基于上下文的多策略进化框架，动态匹配不同天气与负荷场景。
- 全年回测显示能耗降低18.7%，优于ASHRAE标准基准。
- 适合需要高适应性的智能楼宇能源控制系统研发者参考。
本文提出一种情境质量-多样性进化强化学习控制器CQD-ERL，用于热带地区水冷冷水机组及其空气侧的上层控制。该控制器不收敛于单一策略，而是维护一个由数据驱动的操作情境（包括日均气象与负荷模式）和不变行为描述符共同索引的专用策略库，通过无梯度进化算子与软演员-评论家策略梯度算子共享一个经验回放缓冲区实现填充。每次动作执行前均经确定性安全屏障过滤。控制器在代表新加坡商业建筑潜在负荷、冷却塔接近温度及湿度约束的两层简化模型上训练，并在完整年度回测中与ASHRAE Guideline 36基准进行对比。
原文摘要 · Abstract (English)
This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL, for the supervisory control of a tropical, water-cooled chiller plant and its associated air side. Rather than converging to a single scalarised policy, the controller maintains a product archive of specialised policies indexed jointly by a data- driven operating context, a cluster of daily weather and load regime, and a context-invariant behaviour descriptor, filled by a gradient-free evolutionary operator and a soft-actor-critic policy-gradient operator that share one replay buffer. Every action is filtered through a deterministic safety shield before execution. The controller is trained on a two-tier reduced-order environment representing the latent load, cooling-tower approach and humidity constraints of a Singapore commercial building, and is evaluated over a full annual backtest against an ASHRAE Guideline 36 baseline.
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