不确定环境下智能大厦空调系统调度策略评估
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国家自然科学基金(91418203,61202103,61202104);上海市教委创新项目(14ZZ047);上海市知识服务平台(ZF1213)


Formal Evaluation of Scheduling Strategies for Smart Building Air-Conditioning Systems under Uncertain Environment
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National Natural Science Foundation of China (NSFC) (91418203, 61202103, 61202104); Innovation Program of Shanghai Municipal EducationCommission (14ZZ047); Shanghai Knowledge Service Platform (ZF1213)

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    摘要:

    近年来,智能大厦的概念在国内外受到了高度的关注.相比于传统的建筑,智能大厦更加节能、舒适、易维护,已成为未来建筑的发展趋势.作为智能大厦空调通风系统的关键部分,空调系统及其调度策略决定了大厦整体的节能效果以及大厦中用户的舒适度.然而,由于智能大厦所处的环境具有许多不确定因素,极大地增加了空调系统调度策略设计与评估的复杂程度.因此,如何设计与评估不确定环境下空调系统的调度策略成为了智能大厦设计者面临的一大挑战.已有的方法主要针对智能大厦空调系统进行能耗与性能等方面的分析,但尚未有方法针对调度策略本身进行分析与评估.提出一种基于价格时间自动机的调度策略评估框架,支持对不确定环境下的智能大厦进行精确建模与定量评估.该框架使用UPPAAL-SMC作为属性查询引擎对模型进行随机模拟运行,根据模拟结果对不同调度策略下大厦的能耗及用户的舒适度进行定量分析.实验结果表明,该方法能够有效地帮助设计者进行策略的制定和选取.

    Abstract:

    In recent years people have witnessed an increased worldwide attention to the concept of smart buildings.Compared with traditional counterpart, smart buildings are more energy efficient, comfortable and maintainable.Hence, smart buildings are becoming the mainstream of future building construction.As a key part of smart building ventilation systems, air conditioners highly impact the overall energy consumption of smart buildings as well as the experience of their occupants.Therefore, how to design and evaluate feasible scheduling strategies of air conditioning systems becomes a major challenge in the design of smart buildings.Especially when many uncertain factors caused by physical environment are involved, the complexity of strategy evaluation increases drastically.Although existing approaches allow the evaluation of smart buildings from the perspectives of energy consumption and performance, few of them consider the evaluation of the scheduling strategies themselves.Based on priced timed automata, this paper proposes an efficie framework that enables accurate modeling and evaluation of scheduling strategies of smart building air-conditioning systems with uncertain environment.This framework utilizes the statistical model checker UPPAAL-SMC as the engine to quantitatively analyze user-specified performance queries in the form of properties.Based on the underlying random simulation runs monitored by UPPAAL-SMC, the framework can automatically report the quantitative analysis results of energy consumption and user satisfaction under uncertain environment.Experimental results show that the proposed approach can effectively help smart building designers to make their decisions in the selection and optimization of scheduling strategies.

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陈铭松,顾璠,徐思远,陈小红.不确定环境下智能大厦空调系统调度策略评估.软件学报,2016,27(3):655-669

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  • 收稿日期:2015-07-28
  • 最后修改日期:2015-10-20
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  • 在线发布日期: 2016-01-06
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