使用神经网络和基因算法对能源碳和舒适度进行建筑改造多目标优化
Zhongcheng Duan1, Binhao Li1, Yilun Zi1
1School of Architecture and Design, China University of Mining and Technology, Jiangsu, China.
Scientific reports
|October 31, 2025
概括
本研究介绍了节能工业建筑改造的优化框架,平衡热舒适度,能源使用和碳排放. 这种方法可显著减少对可持续改造的不适,能源消耗和生命周期碳排放.
科学领域:
- 建筑科学 建筑科学
- 可持续工程 可持续工程
- 计算优化计算优化
背景情况:
- 工业建筑需要节能改造才能实现全球气候目标.
- 现有结构在提高能源性能和乘客舒适性方面存在挑战.
- 需要采用综合方法来优化改装策略.
研究的目的:
- 开发和验证工业建筑改造的多目标优化框架.
- 评估不同代用建模技术对计算效率的性能.
- 确定最佳的改装解决方案,平衡能源,舒适性和碳排放.
主要方法:
- 使用DesignBuilder对能源,舒适性和排放进行建筑性能模拟.
- 发展和比较反向传播神经网络 (BPNN) 和支持向量回归 (SVR) 替代模型.
- 应用非主导排序基因算法III (NSGA-III) 和权TOPSIS用于多目标决策分析.
主要成果:
- 在代理模型中,BPNN显示出比SVR更高的预测准确度.
- 优化后装备策略减少了10.06%的热不适时间.
- 在能源密度指数 (35.45%) 和生命周期碳排放 (28.86%) 中实现了显著的减少.
结论:
- 拟议的优化框架对现有工业建筑的低碳改造是有效的.
- 该研究提供了一种实际的决策支持工具,用于平衡竞争中的改造目标.
- 综合方法促进了可持续和节能建筑的升级.
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