基于Rhino+Grasshopper的被动式建筑节能因子多目标优化研究OA

The article proposes a systematic framework that integrates orthogonal experimental design and multi-objective genetic algorithm.Taking a 6-story residential building in Nanjing as a case study,the Rhino+Grasshopper platform and Ladybug,Honeybee,Octopus tools are integrated to carry out multi-parameter coupling optimization of window-to-wall ratio(WWR),shading devices,glass types,and insulation materials.Results indicate that the south-facing window-to-wall ratio is the most significant factor influencing energy consumption,while glass type is the key parameter governing daylighting performance.A shutter width between 0.1 and 0.2 meters achieves an optimal balance between daylighting and energy consumption.At this setting,the space's daylight autonomy rate sDA300/50%increases substantially by 102.8%,with energy consumption rising by only 7.7%.The combination of polyurethane insulation and double Low-E insulating glass is widely distributed within the Pareto solution set,forming the foundational configuration for achieving high performance.In addition,the L18(36)orthogonal table combined with the second-generation non-dominated sorting genetic algorithm(NSGA-Ⅱ)generates a Pareto optimal solution set,verifying the reliability of the lighting energy balance solution.