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Optimization Of Ventilation Systems In Office Environment, Part Ⅱ: Results And Discussions

机译：办公环境中通风系统的优化，第二部分：结果与讨论

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摘要

Whether one considers the issues related to office workers' well-being and productivity or the issues from an energy and environmental perspective, there are clear evidences in favor of improving the quality of office environment. Part I of this paper proposed a simulation-based optimization approach by using computational fluid dynamics (CFD) techniques in conjunction with genetic algorithm (GA), with the integration of an artificial neural network (ANN) for response surface approximation (RSA) and for speeding up fitness evaluations inside GA loop. In this part, the results from data preparation for ANN model construction, ANN training and testing, and sensitivity analysis (regarding the impact of weighting factors in the objective function on the optimization results) are presented. Final optimization results indicate that the present choices of objective function and optimization approach are able to result in great improvement in the design and operation of ventilation systems in an office environment, with the goal of enhancing the thermal comfort and indoor air quality (IAQ) without sacrificing the energy costs of ventilation.
机译：无论是考虑与办公室工作人员的福祉和生产力有关的问题，还是从能源和环境角度考虑的问题，都有明显的证据支持改善办公室环境的质量。本文的第一部分提出了一种基于仿真的优化方法，该方法将计算流体动力学（CFD）技术与遗传算法（GA）结合使用，并集成了人工神经网络（ANN）用于响应面逼近（RSA）和加快GA循环内的适应度评估。在这一部分中，将介绍用于神经网络模型构建，神经网络训练和测试的数据准备以及敏感性分析（关于目标函数中的权重因子对优化结果的影响）的结果。最终的优化结果表明，当前目标函数和优化方法的选择能够极大地改善办公室环境中通风系统的设计和运行，从而提高热舒适度和室内空气质量（IAQ），而无需牺牲了通风的能源成本。

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