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Comparison of Control Strategies for Energy Efficient Building HVAC Systems

机译:高效建筑HVAC系统控制策略的比较

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A framework for the design and simulation of a building envelope and an HVAC system is used to compare advanced control algorithms in terms of energy efficiency, thermal comfort, and computational complexity. Building models are first captured in Modelica [1] to leverage Modelica's rich building component library and then imported into Simulink [15] to exploit Simulink's strong control design environment. Four controllers with different computational complexity are considered and compared: a proportional (P) controller with time varying temperature bounds, a tracking linear quadratic regulator (LQR) controller with time varying tuning parameters, a tracking disturbance-aware linear quadratic regulator (d-LQR) controller with time varying tuning parameters which incorporates predictive disturbance information and a model predictive controller (MPC). We assess the performance of these controllers using two defined criteria, i.e. energy and discomfort measured with appropriate metrics. We show that the d-LQR and MPC, when compared to the P controller, manage to reduce energy by 41.2% and 46% respectively, and discomfort from 3.8 to 0. While d-LQR and MPC have similar performance with respect to energy and discomfort, simulation time in the case of d-LQR is significantly less than the one of MPC.
机译:用于建筑信封和HVAC系统的设计和仿真的框架用于比较能源效率,热舒适度和计算复杂性方面的先进控制算法。建筑模型首先在Modelica [1]中捕获,以利用Modelica的丰富的建筑组件库,然后导入Simulink [15]以利用Simulink的强控设计环境。考虑和比较有不同计算复杂性的控制器:比例(P)控制器,带时间变化的温度界限,具有时间变化调谐参数的跟踪线性二次调节器(LQR)控制器,跟踪干扰感知的线性二次调节器(D-LQR )具有时间变化调谐参数的控制器,该参数包括预测干扰信息和模型预测控制器(MPC)。我们使用两个定义的标准评估这些控制器的性能,即通过适当的指标测量的能量和不适。我们表明,与P控制器相比,D-LQR和MPC分别将能量分别降低41.2%和46%,并且从3.8到0.时不适。而D-LQR和MPC对能量具有相似的性能和D-LQR的情况下的仿真时间明显小于MPC之一。

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