神经网络驱动的建筑自适应表皮产出性能预测方法  

Prediction Method of Adaptive Facade Output Performance Driven by the Neural Network

作　　者：史学鹏 石诚斐 解旭东 汪丽君[2] SHI Xuepeng;SHI Chengfei;XIE Xudong;Wang Lijun

机构地区：[1]青岛理工大学建筑与城乡规划学院 [2]天津大学建筑学院

出　　处：《南方建筑》2024年第8期14-21,共8页South Architecture

基　　金：山东省自然科学基金资助项目(ZR2023QE217):城市居住建筑自适应表皮设计方法研究;山东省自然科学基金资助项目(ZR2020ME218):基于热环境性能提升的山东农村住宅改造碳排放控制机理及低碳策略研究;“十四五”国家重点研发计划项目子课题(2023YFC3807404-3):基于亲和感的空间包容性优化技术。

摘　　要：作为应对环境与能源问题的解决办法,耦合动态光伏遮阳与建筑表皮种植的建筑自适应表皮(Adaptive Facade)为城市可持续性提供了新机会,但如何快速准确预测电能与作物产出是设计前期关键问题之一。为解决此问题,以城市居住建筑为例,提出基于机器学习神经网络模型的产出性能预测方法,以替代传统光伏软件模拟与作物产出估算方法。首先建立由实测数据训练并进行差异性激活函数对比择优的机器学习神经网络预测模型,进而搭建交互界面预测平台。结果显示,与基础案例相比,建筑自适应表皮显著提高室内热舒适时间比,降低室内眩光,且满足家庭年用电需求9.3%~10.9%(新加坡)、8.4%~9.8%(海口)以及家庭全年蔬菜需求32%(新加坡)、27.6%(海口),该预测方法展现了预测过程的便捷性与预测结果的可靠性,推动了建筑自适应表皮在可持续城市人居环境建设领域的应用。Adaptive facades that integrate dynamic photovoltaic shading systems and facade planting systems offer new opportunities for urban sustainable development.Through the integration of light,wind,and heat production,adaptive facades can improve indoor environmental quality and generate electricity and crops,thereby reducing buildings'reliance on external resources.However,various environmental factors influence adaptive facades'performance,leading to significant discrepancies between traditional simulation methods and actual results.Predicting electrical energy and crop output quickly and accurately in the design stage has become a key challenge.To address this issue,an output prediction method based on machine learning neural networks was proposed with comprehensive consideration of influences on the urban built environment for adaptive facade dynamic photovoltaic shading and facade planting of urban residential buildings.This method is expected to replace traditional photovoltaic software simulation and crop output estimation methods.Specifically,this method trains an artificial neural network based on measurement data to develop two prediction models.The first model(prediction model of environmental elements and dynamic photovoltaic shading electricity output elements)used Pearson correlation analysis to obtain three environmental factors and one photovoltaic shading power output factor to train and establish the artificial neural network model.Then,the shadow loss coefficient was added to establish the prediction model.Similarly,the second model(prediction model of environmental elements and facade planting crop output elements)sought to establish the correlation between built environmental factors and output factors and choose the optimal model through the comparative selection of difference activation function.An interactive interface prediction platform was established to improve the convenience of the prediction process and the reliability of results.Based on the Rhinoceros+Grasshopper tool,the platform—wh

关 键 词：建筑自适应表皮 城市居住建筑 神经网络 建筑光伏一体化 建筑农业一体化 预测方法 

分 类 号：TU201.5[建筑科学—建筑设计及理论] TU18