每个空调分区一个主动推理智能体：去中心化HVAC边缘控制研究
One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware
这篇论文让每个空调分区跑一个不通信的小智能体，15个挤在一块树莓派5上就把大楼峰值电费降了4%，做楼宇控制或边缘部署的可以看看。
论文提出为多区HVAC系统的每个空调分区分配一个独立的主动推理智能体，智能体之间不通信，通过潜在扰动和基于其他楼层电表的拥塞信号处理未观测的共享风道耦合。在 EnergyPlus 的 Sinergym 办公楼场景中，15个智能体与3种规则控制器和3组 MADDPG 对比，在5个未见过的噪声天气年上取得最低平均建筑峰值功率88.3 kW，比基线低3%到17%，错峰系数0.929优于基线的0.936到0.994。在与某反应式规则舒适度相近的情况下，每月峰值总和降低33 kW（4%），代价是能耗增加约10%。整套系统在单个 Raspberry Pi 5 上运行，MQTT 闭环延迟 p95 为442毫秒，远低于15分钟的控制步长。
One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware
Multi-zone HVAC control is a shared-resource problem: zones on a floor share one fan and cooling coil, so each zone's demand affects its neighbours and the building's peak power. One controller per zone scales well and isolates faults, but each controller then observes only its own zone, not the shared air loop. In this work, we present a decentralised controller that assigns each zone an independent active-inference agent, which plans by minimising expected free energy and learns its zone dynamics online. The agents exchange no messages, so none depends on peers or a coordinator; each accounts for the unobserved coupling through a latent disturbance and a congestion signal derived from the metered power of the other floors. Fifteen agents are evaluated in EnergyPlus on a Sinergym office building against three rule-based controllers and three MADDPG fleets, one given the same floor meters. On five noisy weather years unseen during training, the fleet attains the lowest mean building peak (88.3 kW, 3 to 17 % below the baselines) and staggers the floor peaks better than any baseline, with a coincidence factor (building peak over the sum of the floor peaks) of 0.929 versus 0.936 to 0.994. Compared with a reactive rule at similar comfort (0.912 versus 0.916), it lowers the sum of monthly peaks, on which demand charges are billed, by 33 kW (4 %) in every year, at the cost of about 10 % more energy. It costs 9% less per year than the proactive rule (at lower comfort) and 1% less than the MADDPG fleet given the same floor meters, in every test year. The whole fleet runs on a single low-cost Raspberry Pi 5, closing the observation-to-action loop over MQTT in 442 ms at p95, far below the 15-minute control step.