System optimization for HVAC energy management using the robust evolutionary algorithm.
System optimization for HVAC energy management using the robust evolutionary algorithm.
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DOI：
10.1016/j.applthermaleng.2008.11.019
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发表时间：
2009-08
影响因子：
6.4
通讯作者：
K. F. Fong;V. Hanby;T. Chow
中科院分区：
文献类型：
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作者：
K. F. Fong;V. Hanby;T. Chow
对于已安装的中央供暖、通风和空调系统，适当的能源管理措施将通过优化控制和运行来实现节能目标。常规HVAC系统的性能优化可以通过操作经验来处理，但是其可能不覆盖响应于各种负载和天气条件的不同优化场景和参数。在这方面，通常应用合适的模拟优化技术来对系统进行建模，然后确定所需的操作参数。具体的设备仿真模型可以通过使用可用的仿真程序或数学表达式系统来建立。为了处理仿真模型，数值求解方法将涉及迭代。由于方程的复杂性，梯度信息不易获得，传统的基于梯度的优化方法不适用于这类系统模型。对于启发式优化方法，通常需要连续搜索，并且每次搜索都需要调用系统函数。然后，模拟函数调用的频率将是决定时间的步骤，并且有效的优化方法是至关重要的，以便在合理的计算周期内通过许多函数调用找到解决方案。在本文中，鲁棒进化算法（REA），以解决这一性质的暖通空调仿真模型。REA是基于进化算法的一个范例，进化策略，这是一个随机的基于种群的搜索技术，强调变异。REA算法结合了柯西确定性变异、竞赛选择和算术重组等技术，为最优搜索提供了协同效应。REA是有效的，以科普复杂的仿真模型，以及那些表示为显式数学表达式的暖通空调工程优化问题。
For an installed centralized heating, ventilating and air conditioning (HVAC) system, appropriate energy management measures would achieve energy conservation targets through the optimal control and operation. The performance optimization of conventional HVAC systems may be handled by operation experience, but it may not cover different optimization scenarios and parameters in response to a variety of load and weather conditions. In this regard, it is common to apply the suitable simulation–optimization technique to model the system then determine the required operation parameters. The particular plant simulation models can be built up by either using the available simulation programs or a system of mathematical expressions. To handle the simulation models, iterations would be involved in the numerical solution methods. Since the gradient information is not easily available due to the complex nature of equations, the traditional gradient-based optimization methods are not applicable for this kind of system models. For the heuristic optimization methods, the continual search is commonly necessary, and the system function call is required for each search. The frequency of simulation function calls would then be a time-determining step, and an efficient optimization method is crucial, in order to find the solution through a number of function calls in a reasonable computational period. In this paper, the robust evolutionary algorithm (REA) is presented to tackle this nature of the HVAC simulation models. REA is based on one of the paradigms of evolutionary algorithm, evolution strategy, which is a stochastic population-based searching technique emphasized on mutation. The REA, which incorporates the Cauchy deterministic mutation, tournament selection and arithmetic recombination, would provide a synergetic effect for optimal search. The REA is effective to cope with the complex simulation models, as well as those represented by explicit mathematical expressions of HVAC engineering optimization problems.