Low-carbon design: Building optimization considering carbon emission, material utilization, and daylighting

Abstract

In this study, a low-carbon architectural design method is proposed. With the proposed method, it is possible to minimize the carbon emissions of the building while optimizing its material utilization and daylighting. The design process consists of three main steps: Firstly, a parametric model of the building was constructed to determine its material utilization, daylighting, and carbon emission under different parameters. The model's accuracy was then experimentally validated. Secondly, establish neural networks between structural parameters (geometry and window-to-wall ratio) and material utilization, daylighting, and carbon emissions. Subsequently, perform a multi-objective optimization of the building's carbon emissions, material utilization and daylighting based on the established neural network. Finally, a modified “Technique for Order Preference by Similarity to an Ideal Solution” (TOPSIS) is proposed to determine the optimal solution among multiple optimization results. To illustrate the optimization process of the proposed method in detail, it is applied to a real case of greenhouse optimization. To holistically evaluate the optimization, a comprehensive evaluation index, Pc, that considers material utilization, daylighting, and carbon emissions, is proposed. The study reveals that upon utilizing the proposed low-carbon optimal design method, the Pc of the greenhouse sees a 7.67-fold increase, while simultaneously reducing carbon emissions by 23% compared to original levels. Additionally, specific greenhouse designs were optimized to demonstrate the method's practicality.