摘要:天然光营造的光环境以经济、自然、宜人、不可替代等特性为人们所习惯和喜爱。天然采光不仅有利于照明节能，而且有利于增加室内外的自然信息交流，改善空间卫生环境，调节空间使用者的心情。在建筑中充分利用天然光，对于创造良好光环境、节约能源、保护环境和构建绿色建筑具有重要意义。因此，优化建筑采光设计是很有必要的。本文提出了一个基于BIM技术和BP神经网络的建筑物天然采光分析思路，以成都理工大学图书馆为例，利用Revit软件建立三维可视化模型，生成gbXML格式的建筑物信息文件，再将gbXML文件导入Ecotect软件，在Ecotect软件内对图书馆的室内光环境进行模拟分析，计算自然采光系数，并定量分析窗台高度、玻璃透光率和墙体材料光反射率对室内光环境的影响。最后借助Weka软件，建立基于BP算法的神经网络模型，得到可预测在最优采光系数下变量变化范围的BP神经网络模型。

Abstract: The light environment created by natural light is preferred by public due to its own economic, natural, pleasant and irreplaceable characteristics. Natural lighting is not only conducive to energy conservation of lighting, but also conducive to increasing the exchange of natural information indoor and outdoor, improving the space health environment and regulating the mood of space users. Making full use of the natural light in the building is of great significance for creating a good light environment, saving energy, protecting the environment and building green buildings. Therefore, it is necessary to optimize the lighting design of buildings. This paper puts forward an idea of natural lighting analysis in building based on the BIM technology and the BP neural network. Taking the CDUT library (library of Chengdu University of Technology) as an example, a 3D visualization model is established by using Revit software to generate the building information file in gbXML format. Then, the gbXML file is imported into Ecotect software to simulate and analyze the indoor light environment of the library, to calculate the natural lighting coefficient, and to quantitatively analyze the impact of window height, glass transmittance and wall material light reflectivity on the indoor light environment. At last, with the help of Weka software, the neural network model based on BP algorithm is established, obtaining the BP neural network model which can predict the variation range of variables under the optimal lighting coefficient.