The intelligent sunshade blind control system based on the predictive model has the capability to reduce glare performance in indoor architectural spaces,and its intelligence depends on the control method employed.The design of control methods depends on thoughtful consideration of information related to the impact on indoor lighting performance.Different influencing information serves as the basis for control feedback and is also essential for constructing intelligent prediction models.This study uses machine learning techniques to conduct a glare correlation analysis for various blind control variables,as well as to explore the interactive effects of different control variables on glare impact.XGBoost was used to conduct feature selection and build a prediction model,while SHAP was used to further analyze the correlation magnitude between variables and glare.The results show that the distance between the indoor user's location and the sunshade louvers,the deformation of the sunshade,and glare are closely related.There is a significant interactive effect,and the solar altitude angle is also an important response information for the sunshade louvers.The conclusions can provide a reference basis for the design of intelligent sunshade louver control systems and the construction of prediction models.
维普
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