| 引用本文: | 刘岳,吴亚琦,韩臻,康钰卓,刘刚.建筑矩阵型性能数据预测方法及其评价[J].哈尔滨工业大学学报,2022,54(11):59.DOI:10.11918/202110101 |
| LIU Yue,WU Yaqi,HAN Zhen,KANG Yuzhuo,LIU Gang.Prediction method of building matrix performance data and its evaluation[J].Journal of Harbin Institute of Technology,2022,54(11):59.DOI:10.11918/202110101 |
|
| |
|
|
| 本文已被：浏览 822次 下载 1339次 |
码上扫一扫！ |
|
|
| 建筑矩阵型性能数据预测方法及其评价 |
|
刘岳1,3,吴亚琦2,3,韩臻2,3,康钰卓2,3,刘刚2,3
|
|
(1.天津大学 国际工程师学院,天津 300072;2.天津大学 建筑学院,天津 300072; 3.天津市建筑物理环境与生态技术重点实验室(天津大学),天津 300072)
|
|
| 摘要: |
| 建筑室内光环境、风环境、温湿度场等建筑物理环境会对使用者的生活品质产生较大影响,室内物理环境设计显得尤为重要。代理模型是一种有效的建筑性能快速预测方法,可以帮助建筑师在设计初期进行方案筛选或迭代调整,但现有代理模型大多是对能耗、采光系数等“单值型”建筑性能进行计算,而对照度分布、风速场、温湿度场等“矩阵型”数据（即云图）的计算效果并不佳。为解决上述问题,本文提出了一种基于条件生成式对抗网络（CGAN）的建筑性能预测方法,基于pix2pix算法构建建筑照度、风速场云图的CGAN模型,建立图像至图像间的映射关系,通过输入建筑轮廓图进而生成对应平面的性能数据,从而实现对建筑性能的快速预测,提升建筑初期设计效率。同时,为评估预测结果的准确性,本文建立了基于通道直方图法和灰度值比较法的评价体系,对本文结果进行了相似度验证。结果表明:训练后的建筑CGAN模型可在1 s内快速绘制云图,速度远高于传统仿真软件,且预测结果与仿真计算结果高度吻合,二者相似度可达93.82%（照度）和82.99%（风速场）,证明了基于CGAN模型的建筑室内矩阵型数据预测方法的有效性。 |
| 关键词: 建筑性能预测 条件生成式对抗网络 代理模型 验证评价 |
| DOI：10.11918/202110101 |
| 分类号:TU18 |
| 文献标识码:A |
| 基金项目:教育部产学合作协同育人项目(202002064039) |
|
| Prediction method of building matrix performance data and its evaluation |
|
LIU Yue1,3,WU Yaqi2,3,HAN Zhen2,3,KANG Yuzhuo2,3,LIU Gang2,3
|
|
(1.Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, China; 2. School of Architecture, Tianjin University, Tianjin 300072, China; 3. Tianjin Key Laboratory of Architectural Physical Environment and Ecological Technologies (Tianjin University), Tianjin 300072, China)
|
| Abstract: |
| The indoor physical environment of buildings such as light environment, wind environment, and temperature and humidity field has great impact on the life quality of users, so the design of indoor physical environment is especially important. Surrogate model is an effective rapid prediction method of building performance, which can help architects in selecting or iteratively adjusting the plan at the initial stage of design. However, most existing surrogate models focus on calculating single valued data such as energy consumption and daylighting coefficient, lacking of calculation methods for “matrix” data (i.e. cloud map) such as illuminance distribution, wind field, and temperature and humidity field. To this end, a building performance prediction method was proposed combined with the conditional generative adversarial network (CGAN). Based on the pix2pix algorithm, the CGAN model of cloud images of illuminance and wind field was constructed, and the mapping relationship between the images was established. The performance data of the corresponding plane were generated by inputting the building outline, so as to rapidly predict the building performance and improve the efficiency of the initial design of the building. In order to evaluate the accuracy of the prediction results, we established an evaluation system based on the channel histogram method and the grayscale value method, which was applied for similarity verification. Results show that the trained architectural CGAN model could quickly draw cloud images within 1 s, which was much faster than traditional simulation software, and the prediction results were highly consistent with the simulation calculation results. The similarity between the results reached 93.82% (illuminance) and 82.99% (wind field), which proved the effectiveness of the prediction method of building indoor matrix data based on CGAN model. |
| Key words: building performance prediction conditional generative adversarial network surrogate model validation evaluation |
|
|
|
|
