住宅供暖的无模型控制：非线性数据驱动预测控制的部署与仿真
Model-Free Control for Residential Heating: Deployment and Simulation of Nonlinear Data-Enabled Predictive Control
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中文总结 AI 辅助
针对住宅供暖中MPC建模困难的问题，本文应用三种非线性DeePC（DPC）方法，在数字孪生和真实公寓中验证其比滞回控制节能约11%，且优于线性DeePC。
中文摘要 AI 辅助
住宅供暖在建筑能耗中占比很大，预测控制策略可以通过预测而非仅对室温做出反应来降低能耗。实施预测控制的通常方式是模型预测控制（MPC），它需要为每个住宅单元建立精确的供暖控制模型。手动获取该模型耗时甚至不可能，且此类模型无法在异构建筑群中迁移。数据驱动预测控制（DeePC）通过直接利用系统测量轨迹设计控制器，省去了建模步骤。然而，DeePC的基础建立在确定性线性时不变系统类别之上，这对于住宅供暖而言是不现实的假设。本研究应用了三种最近提出的DeePC非线性扩展，统称为数据驱动预测控制（DPC）：选择式DPC（Select-DPC）、增益调度DPC（gain-scheduling DPC）和线性参数变化DPC（linear parameter-varying DPC）。这些方法与标准DeePC以及行业标准的滞回控制器（hysteresis controller）在住宅供暖中进行了对比。对比在瑞士NEST研究建筑中一个被占用的研究单元的数字孪生模型上运行了完整供暖季，通过供暖能耗和舒适度带违规进行评估。此外，增益调度DPC被部署在真实住宅公寓中，验证了仿真结论。每种DPC控制器的能耗均显著低于滞回控制器，整个供暖季约节省11%的能耗，验证了DPC的有效性。非线性方法在所有评估场景中均优于线性DeePC，我们分析了每种非线性方法的相对成本和收益。
英文摘要
Residential heating accounts for a large share of building energy use, and predictive control strategies can reduce it by anticipating rather than reacting to the room temperature alone. The usual manner of implementing predictive control, model predictive control (MPC), requires an accurate model of each individual residential unit for heating control. Obtaining this model is time-consuming to obtain manually or even impossible, and such models do not transfer across a heterogeneous building stock. Data-enabled predictive control (DeePC) removes this modeling step by designing the controller directly using measured trajectories of the system. The foundations of DeePC, however, are built on the class of deterministic linear time-invariant systems, which is an unrealistic assumption for residential heating. This work applies three recently proposed nonlinear extensions of DeePC, collectively referred to as data-driven predictive control (DPC): Select-DPC, gain-scheduling DPC, and linear parameter-varying DPC. These are compared against standard DeePC and the hysteresis controller, the industry standard in residential heating. The comparison runs over a full heating season on a calibrated digital twin of an occupied research unit, the NEST research building in Switzerland, assessed by heating energy and comfort-band violation. Moreover, gain-scheduling DPC was deployed on the real residential apartment and validated the conclusions from the simulations. Each of the DPC controllers consumes significantly less energy than the hysteresis controller, amounting to roughly $11\%$ over the season, validating the use of DPC. The nonlinear methods further outperform linear DeePC in all assessed scenarios, and we analyze the relative costs and benefits of each nonlinear method.
发表机构
- University of Oslo（奥斯陆大学）
- Swiss Federal Laboratories for Materials Science and Technology (Empa)（瑞士联邦材料科技与工程研究院）
- University of Pennsylvania（宾夕法尼亚大学）
- Eindhoven University of Technology（埃因霍温理工大学）
- University of Groningen（格罗宁根大学）
机构由 AI 辅助整理，请以论文原文为准。
