Limited by the climate, economic level and energy-saving awareness of rural residents in the western China, rural houses are still in a stage of high energy consumption (EC) and low comfort. The passive design strategies should be given priority to provide an optimal design mode. The challenge is to find the best combination of design parameters. In this paper, rural single-storey detached houses are taken as the research objects. Three conflicted indicators are set as the objective function, namely building EC, thermal comfort and economy. The investigated design variables include building orientation, insulating layer thickness, window width & type, and indoor design temperature. A methodology of combining EnergyPlus and MOBO is used for multi-objective optimization, thus getting the Pareto solutions and using the weighted sum method to obtain the optimum parameter combinations. The proposed methodology for simulation-based multi-objective optimization is a useful tool to facilitate decision-making in building design.

Highlights

• An automated optimization method of combining EnergyPlus simulation software and MOBO optimization engine is proposed.

• The multi-objective method optimizes the building orientation, envelope and winter indoor temperature.

• Pareto non-dominated solutions for three conflict objective functions are obtained.

• The final optimum combination is determined by a weighted sum method.

受气候、经济水平和西部农村居民节能意识的限制，农村住宅仍处于高能耗（EC）和低舒适度的阶段。应优先考虑被动设计策略，以提供最佳设计模式。挑战在于找到设计参数的最佳组合。本文以农村单层独立屋为研究对象。设定三个相互矛盾的指标作为目标函数，即建筑EC、热舒适性和经济性。研究的设计变量包括建筑方向、绝缘层厚度、窗户宽度和类型以及室内设计温度。结合 EnergyPlus 和 MOBO 的方法用于多目标优化，从而得到帕累托解，并使用加权和法获得最优参数组合。所提出的基于仿真的多目标优化方法是促进建筑设计决策的有用工具。

强调

• 提出了一种将EnergyPlus仿真软件与MOBO优化引擎相结合的自动化优化方法。

• 多目标方法优化建筑朝向、围护结构和冬季室内温度。

• 获得了三个冲突目标函数的帕累托非支配解。

• 最终的最优组合由加权和法确定。