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Reduced energy consumption and enhanced comfort with smart windows: Comparison between quasi-optimal, predictive and rule-based control strategies

机译：使用智能窗户可降低能耗并提高舒适度：准最佳，预测和基于规则的控制策略之间的比较

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Smart windows are used to reduce energy consumption and improve thermal and visual comfort mainly by controlling the solar flux entering into a building. This article presents a simulation study in which the impact of the applied control strategy on the overall energy consumption (heating, cooling and lighting) is investigated. A commercial building located in Montreal (Canada) with south-oriented integrated electrochromic windows is modeled. The hour-by-hour state of the smart windows required to minimize overall energy consumption while respecting constraints related to thermal and visual comfort is determined through an optimization strategy based on genetic algorithms (GA). Then, this quasi optimal control is compared to other approaches that could be applied in real-time applications: (i) two types of rule-based controls (RBC), i.e. RBC1 and RBC2 and (ii) a model predictive control (MPC). The impacts of thermal mass and installed light power density are also analyzed. Results show that the four control strategies under study presented similar energy consumption with differences in total energy consumption ranging from 4% to 10%. While more complex controllers such as MPC could potentially lead to improved performances considering more design variables, complex models and extensive commissioning, this study illustrates that simpler control strategies such as RBC2 can also lead to satisfying results. (C) 2016 Elsevier B.V. All rights reserved.
机译：智能窗户主要用于控制进入建筑物的太阳光通量，以减少能耗并改善热和视觉舒适度。本文提供了一个仿真研究，其中研究了应用控制策略对整体能耗（加热，冷却和照明）的影响。对位于加拿大蒙特利尔的一幢商业建筑进行建模，该建筑具有朝南的集成电致变色窗。通过基于遗传算法（GA）的优化策略，可以确定智能窗户的每小时状态，以最大限度地减少总体能耗，同时遵守与热舒适性和视觉舒适性相关的约束。然后，将此准最优控制与可以应用于实时应用的其他方法进行比较：（i）两种基于规则的控制（RBC），即RBC1和RBC2，以及（ii）模型预测控制（MPC） 。还分析了热质量和安装的光功率密度的影响。结果表明，所研究的四种控制策略呈现出相似的能耗，总能耗差异在4％至10％之间。尽管考虑到更多的设计变量，复杂的模型和广泛的调试，更复杂的控制器（例如MPC）可能会提高性能，但这项研究表明，更简单的控制策略（例如RBC2）也可以带来令人满意的结果。 （C）2016 Elsevier B.V.保留所有权利。

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