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Model Predictive Control for Optimal Integration of a Thermal Chimney and Solar Shaded Building*

机译:热烟囱和太阳阴凉建筑的最佳集成模型预测控制*

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Energy-saving devices are extensively sought in several fields, including heating, ventilation, and air-conditioning (HVAC) tasks in buildings. This paper investigates six model predictive control (MPC) strategies as a way to optimize the operation of a solar shaded, natural ventilated building located at TU Delft campus. Such building is based on an innovative combination of a thermal chimney and glazing walls for harvesting passive energy. The main challenge is dealing with the system dynamics. The predictive controllers we consider include both linear and nonlinear MPC, and four hierarchical MPC strategies. All controllers aim to minimize auxiliary power consumption and, consequently, increasing the energy savings. The six control strategies performance are evaluated using reference values for thermal comfort, while relying on simulations performed in MATLAB for calculations. The hierarchical MPC architecture which considers a hybrid structure with nonlinear tracker for ventilation and linear agents for heating purposes appears the most promising one.
机译:在几个领域中广泛寻求节能器件,包括建筑物中的加热,通风和空调(HVAC)任务。本文调查了六种模型预测控制(MPC)策略,作为优化位于涂德尔福特校园的太阳阴影,自然通风建筑的运行。这种建筑基于用于收获被动能量的热烟囱和玻璃壁的创新组合。主要挑战正在处理系统动态。我们考虑的预测控制器包括线性和非线性MPC和四个分层MPC策略。所有控制器旨在最大限度地减少辅助功耗,从而提高节能。使用热舒适度的参考值评估六种控制策略性能,同时依赖于MATLAB执行的模拟以进行计算。认为具有用于供气和线性代理的非线性跟踪器的混合结构的分层MPC架构出现了用于加热目的最有前途的。

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