Improving building performance is of great significance to protecting the ecology, saving energy and enhancing the living environment. This paper developed a comprehensive method for optimizing the design of a regular architectural space to improve building performance. The research aims to provide architects with robust and accurate design references when conducting design tasks. The entire optimization process is divided into three steps. The first step is to build a database by generating the research objects randomly and performing building simulations on them. The second step is to train artificial neural network (ANN) models as a substitute of the time-consuming building simulation in the multi-objective optimization to predict the building performance quickly. The last step is to perform multi-objective optimization based on the actual design constraints. To demonstrate the optimization process, a common type of classroom space defined by 30 design parameters was selected as a case study. The optimization objectives were set as energy demand, thermal environment and daylight environment. The accuracy of different ANN models was assessed. To imitate realistic design tasks, three design scenarios with constraints are used in the optimization. All the three optimizations finished in 350 s. Compared to the traditional optimization method based on simulation, the optimization calculation is accelerated by approximately 2,570 times. To ensure the reliability of the optimization results, several nondominated solutions of each case were validated by simulation, and there were good agreements between the simulation results and the optimization results. An integrated solution and a reference solution was defined for each case. Compared to the reference solution, the objectives of the integrated solution of the three cases have been improved by 24.6%, 18.7% and 14.2% on average, respectively, indicating that this method is feasible and effective to improve building design in actual tasks.

中文翻译：

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优化常规建筑空间设计以提高建筑性能的综合方法

提高建筑性能对于保护生态、节约能源、改善人居环境具有重要意义。本文开发了一种优化常规建筑空间设计以提高建筑性能的综合方法。该研究旨在为建筑师在执行设计任务时提供稳健且准确的设计参考。整个优化过程分为三个步骤。第一步是通过随机生成研究对象并对其进行构建模拟来构建数据库。第二步是训练人工神经网络（ANN）模型来代替多目标优化中耗时的建筑模拟，以快速预测建筑性能。最后一步是根据实际设计约束进行多目标优化。为了演示优化过程，选择了由 30 个设计参数定义的常见类型的教室空间作为案例研究。优化目标设定为能源需求、热环境和日光环境。评估了不同 ANN 模型的准确性。为了模仿现实的设计任务，在优化中使用了三个带有约束的设计场景。所有三项优化均在 350 秒内完成。与传统基于仿真的优化方法相比，优化计算速度加快约2570倍。为保证优化结果的可靠性，对每种情况下的几个非支配解进行了仿真验证，仿真结果与优化结果吻合较好。为每种情况定义了集成解决方案和参考解决方案。与参考解决方案相比，三个案例的综合解决方案的目标平均分别提高了24.6%、18.7%和14.2%，表明该方法在实际任务中改进建筑设计是可行和有效的。