Application of Artificial Neural Network for Predicting Indoor Annual Dynamic Daylighting
BAI Xue
WU Wei
WU Nong
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.
Keywords：early-stage architectural designartificial neural networksyear-round dynamic lightingneural network weighting analysis
Publication Date：2024-08-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 81-87 )