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Distributed model predictive control based on Benders' decomposition applied to multisource multizone building temperature regulation

机译:基于弯道分解的分布式模型预测控制应用于多源多态建筑温度调节

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This paper presents a distributed model predictive control algorithm based on Benders' decomposition for temperature regulation in buildings. It is well known that the main objective of this control problem is to minimize the heating (cooling) energy bills while maintaining a certain indoor thermal comfort. In order to reduce the energy costs, many buildings are equipped with several heating sources with different dynamics, gains and energy prices, an example is the use of a hot water based central heating and local electric convectors as a complementary heating source. Using a linear system model of the controlled process, the MPC minimization problem can be solved by linear programming. The Benders' decomposition exploits a particular structure (block-angular) of the constraint matrix and distributes the computational demand among local controllers. The effectiveness of the proposed distributed control approach comparing to the currently used PI-based control is illustrated in a simulation study.
机译:本文介绍了基于弯管分解的分布式模型预测控制算法,用于建筑物温度调节。众所周知,该控制问题的主要目的是最小化加热(冷却)能量纸币,同时保持一定的室内热舒适度。为了降低能源成本,许多建筑物配备了几种具有不同动力学,提升和能源价格的加热来源,示例是使用热水基的中央加热和局部电流作为互补的加热源。使用受控过程的线性系统模型,可以通过线性编程来解决MPC最小化问题。弯曲者的分解利用约束矩阵的特定结构(块角)并分发本地控制器之间的计算需求。在模拟研究中说明了与当前使用的基于PI的控制的提出的分布式控制方法的有效性。

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