开放办公空间智能遮阳百叶天然采光眩光预测模型建构研究

Research on the Construction of Glare Prediction Model for Daylighting with Intelligent Sunshade Louvers in Open Office Spaces

在多窗户、多区域的开放办公空间中,利用智能遮阳百叶对天然采光引发的眩光进行调控具有挑战性,既需协同不同区域视觉舒适性最佳,又需考虑不同百叶遮阳形式下的复合影响效应。受惠人工智能技术浪潮,采用机器学习算法建构有效的眩光预测模型成为解决开放办公空间智能遮阳百叶眩光调控的有力手段。基于此,本研究通过相关实验,对不同机器学习算法的眩光预测模型建构性能进行综合对比,得出最佳机器学习算法,以实现有效开放办公空间智能遮阳百叶天然采光眩光预测模型的建构。

In multi-window and multi-zone an open office spaces,it is challenging to regulate the glare caused by natural daylighting using intelligent sunshade louvers.It is necessary to coordinate the best visual comfort in different areas and also consider the combined influence effects under different sunshade forms of louvers.Benefiting from the wave of artificial intelligence technology,using machine learning algorithms to construct effective glare prediction models has become a powerful means to solve the glare regulation of intelligent sunshade louvers in open office spaces.Based on this,through relevant experiments,this study comprehensively compares the construction performance of glare prediction models of different machine learning algorithms and obtains the best machine learning algorithm to achieve the construction of an effective glare prediction model for natural daylighting with intelligent sunshade louvers in open office spaces.