Research on Indoor Illuminance Distribution Map Generation Based on Pix2pix
ZHANG Lihua
BAI Jinni
Han Zhen
LIU Gang
Abstract：In the early stages of indoor lighting design,multiple iterations and rapid acquisition simulation results are often required.To support lighting design and address the problems of high computational load and long processing time associated with traditional indoor illuminance simulation tools,this paper proposes a fast prediction method for indoor illuminance distribution based on Conditional Generative Adversarial Networks(CGAN).First,high-precision workplane illuminance simulation were conducted on 2000 residential building samples using Honeybee to construct a training dataset.Furthermore,based on the Pix2pix algorithm,a corresponding deep learning network architecture was designed.After 200 epochs of training,the model successfully established a mapping from 2D lighting layout diagrams to indoor illuminance distribution maps.The result show that the model achieved a high prediction accuracy,with an average Structural Similarity Index(SSIM)of 0.974 and a Mean Absolute Error(MAE)of 0.014 on the test set.Compared with traditional numerical simulation methods,this approach provides millisecond-level performance feedback,greatly enhancing design efficiency.This study offers an efficient tool for rapid evaluation and optimization in indoor lighting design,contributing significantly to the advancement of green buildings and the enhancement of indoor environmental quality.
Keywords：conditional generative adversarial networksindoor illuminance predictionsurrogate model
Publication Date：2025-08-30
Online Publishing Date：2025-09-17(First online date of this platform, not the publication date of the document)
Pages：9( 139-147 )