Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm
Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm
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DOI：
10.1016/j.energy.2008.10.001
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发表时间：
2009
期刊：
影响因子：
9
通讯作者：
M. Mossolly;K. Ghali;N. Ghaddar
中科院分区：
文献类型：
--
作者：
M. Mossolly;K. Ghali;N. Ghaddar
研究变风量空调系统的优化控制策略。控制策略包括固定温度设定点的基本控制策略和保证舒适性和室内空气品质的两种高级控制策略。第一个高级控制调节新鲜空气供应率和供应空气温度，以保持每个区域的温度设定点，同时确保室内空气质量。第二种策略控制新风量和送风温度，以维持每个区域可接受的热舒适性和IAQ。每个控制策略的优化问题是制定基于能源消耗的成本和系统和热空间瞬态模型的约束。采用遗传算法求解优化问题。优化方案/模型应用于贝鲁特天气的建筑楼层的案例研究。使用Visual DOE 4.0软件[Architectural Energy Cooperation，San弗朗西斯科，USA; 2005年]对基本策略的热空间和系统组件模型进行了验证。在夏季的四个月里，与传统的基本策略相比，优化的高级策略实现了高达30.4%的节能，同时舒适性和室内空气品质也得到了满足。
This paper examines optimal control strategies of variable air volume air conditioning system. The control strategies included a base control strategy of fixed temperature set point and two advanced strategies for insuring comfort and indoor air quality (IAQ). The first advanced control adjusts the fresh air supply rate and the supply air temperature to maintain the temperature set point in each zone while assuring indoor air quality. The second strategy controls the fresh air rate and the supply air temperature to maintain an acceptable thermal comfort and IAQ in each zone. The optimization problem for each control strategy is formulated based on the cost of energy consumption and constrained by system and thermal space transient models. The optimization problem is solved using genetic algorithm. The optimization scheme/model is applied to a case study for a building floor in Beirut weather. The thermal space and system component models were validated for the base strategy using Visual DOE 4.0 software [Architectural Energy Cooperation, San Francisco, USA; 2005 〈www.archenergy.com〉]. Energy savings up to 30.4% were achieved during the summer season of four months with the optimized advanced strategies when compared with the conventional base strategy while comfort and IAQ were satisfied.