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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.
In recent years, the concept of smart building has received a great deal of attention both at home and abroad. Compared with traditional buildings, smart buildings have become more energy-efficient, comfortable and easy to maintain and have become the future trend of buildings. As a key part of intelligent building air-conditioning and ventilation systems , The air-conditioning system and its scheduling strategy determine the overall energy-saving effect of the building and the comfort of users in the building.However, due to the many uncertainties in the environment in which the intelligent building is located, the complexity of the design and evaluation of the air-conditioning system scheduling strategy is greatly increased Therefore, how to design and evaluate the dispatching strategy of air-conditioning system in uncertain environment becomes a major challenge for intelligent building designers.The existing methods mainly focus on the analysis of energy consumption and performance of air-conditioning system in intelligent buildings, There is no method to analyze and evaluate the dispatch strategy itself.We propose a scheduling strategy evaluation framework based on price-time automaton to support the accurate modeling and quantitative assessment of intelligent buildings in uncertain environment.Using UPPAAL-SMC as the framework, Attribute query engine to run a random simulation of the model, according to the simulation results do not Scheduling strategy building energy consumption and user comfort for quantitative analysis. The results show that this method can effectively help designers to select and develop strategies.