照明工程学报2024，Vol.35Issue(4)：81-87,7.DOI:10.3969/j.issn.1004-440X.2024.04.011
应用人工神经网络预测室内全年动态采光
Application of Artificial Neural Network for Predicting Indoor Annual Dynamic Daylighting
白雪 1吴蔚 1吴农2
作者信息
- 1. 南京大学 建筑与城市规划学院,江苏 南京 210093
- 2. 西北工业大学 力学与土木建筑工程学院,陕西 西安 710072
- 折叠
摘要
Abstract
In the early stage of architectural design,understanding the relationship between architectural form parameters and interior daylighting is crucial for design optimization.This study employs a Multilayer Perceptron(MLP)neural network,takes four main features(outdoor occlusion situation,architectural form characteristics,window opening settings,and measurement point location information)as the input parameters of the MLP,and builds the neural network through the data collected by computer simulation to predict the annual indoor natural daylighting quality(UDI<100 lx,UDI100～2000 lx,UDI>2000 lx).The research results demonstrate that the MLP neural network model achieved a regression coefficient R2 of 0.984 and a mean squared error(MSE)of 11.624 on the test dataset,indicating high accuracy.The weight analysis of the neural network reveals that the external shading height and building depth significantly influence the output.In contrast,the elevation of window sills and the distance of measurement points from windows have a minor impact on the results.The neural network model provides a new intelligent approach for predicting daylight in architectural design,assisting in early-stage design decision-making.关键词
建筑设计早期阶段/人工神经网络/全年动态采光/神经网络权重分析Key words
early-stage architectural design/artificial neural networks/year-round dynamic lighting/neural network weighting analysis分类
土木建筑引用本文复制引用
白雪,吴蔚,吴农..应用人工神经网络预测室内全年动态采光[J].照明工程学报,2024,35(4):81-87,7.
