基于遗传算法的建筑节能改造策略多目标优化技术

Shuibo Deng, Lei Lv
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引用次数: 0

摘要

建筑节能设计对行业实现减碳和可持续发展意义重大。首先,基于社会、自然和经济的三维视角,建立了能耗、成本和碳排放的多目标模型。然后,利用多项式算子改进非支配排序遗传算法,计算出最优解集。传统优化过程中算法直接耦合导致的计算效率低下问题有望得到解决。结果显示,对于 Square1 数据集和 Iris 数据集,与支持向量机遗传算法和多目标聚类算法相比,本研究提出的算法在反向距离和收敛性指标上提高了 70% 以上,数值更接近于 0。这验证了本研究建立的多目标模型和求解算法的有效性,有助于获得最优节能设计方案,为建筑低碳优化提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy Based on Genetic Algorithm
Building energy-saving design is significant for the industry to achieve carbon reduction and sustainable development. Firstly, a multi-objective model for energy consumption, cost, and carbon emissions is established based on the three-dimensional perspectives of society, nature, and economy. Then, a polynomial operator is used to improve the non dominated sorting genetic algorithm to calculate the optimal solution set. The low computational efficiency caused by direct coupling of algorithms in traditional optimization processes is expected to be addressed. According to the results, for the Square1 dataset and Iris dataset, the algorithm proposed in this study improved the reverse distance and convergence metrics by more than 70% compared to support vector machine-genetic algorithm and multi-objective clustering algorithm, with values closer to 0. The solution solved by this algorithm had lower building costs, energy consumption, and carbon emissions, with values of 345200 yuan, 2374 KWh/year, and 26 tons, respectively. This validates the effectiveness of the multi-objective model and solving algorithm established in the study, which helps to obtain the optimal energy-saving design scheme and provides reference for low-carbon optimization of building.
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