发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对线性依赖热负荷的目标开发几乎无需调试的HVAC控制器，最多需两个热参数，模拟中性能对参数误差鲁棒，无需调整，能保持舒适度并实现一定比例性能提升，避免传统方法负担，获取先进控制大部分价值。
AI 中文摘要
先进的供暖和制冷系统控制可大幅降低能源成本和污染。然而，诸如模型预测控制（MPC）和强化学习（RL）等在研究人员中流行的算法，在实际应用中仍受限制，部分原因是其高部署和调试成本。在此，我们针对线性依赖受控热负荷的目标，如能源成本和污染，开发了两种几乎无需调试的控制器。这些控制器最多需要两个热参数。在代表性供暖模拟中，控制器性能对大参数规格误差具有鲁棒性，表明无需调整即可部署。控制器在保持良好居住舒适度的同时，实现了全知策略（具有完美模型信息和预测）所达到性能提升的43%至98%（取决于电价和控制器变体）。这些结果表明，简单的、利用结构的控制器可能在避免传统MPC或RL可能产生的数据、建模、调整和计算负担的同时，获取先进控制的大部分可实现价值。
英文摘要
Advanced control of heating, ventilation, and air-conditioning (HVAC) systems can substantially reduce energy costs and pollution. However, real-world adoption of popular algorithms among researchers, such as model predictive control and reinforcement learning, remains limited due in part to their high deployment and commissioning costs. Here, we develop two nearly commissioning-free supervisory controllers tailored to objectives that depend linearly on the controlled thermal load, such as energy costs and pollution. The controllers require, at most, two easily-estimable thermal parameters, forecasts of energy prices and occupant temperature preferences over a prediction horizon, and an indoor temperature measurement. In residential cooling simulations, both controllers perform essentially as well under traditional time-invariant electricity pricing as an omniscient optimal controller with exact model information and perfect forecasts, and attain up to 86.6% of the omniscient cost savings under increasingly prevalent time-varying pricing. These results suggest that simple, structure-exploiting controllers may capture most of the attainable value of advanced supervisory HVAC control with linear objectives, while avoiding the data, modeling, tuning, and computational burdens that hinder real-world deployment.
