Sensitivity analysis and prediction of shading effect of external Venetian blind for nearly zero-energy buildings in China

External Venetian blind shading (EVBS) is identified as an effective technology for reducing cooling demand in summer. Inappropriate design of the fenestration component may led to low energy efficiency, and there lacks simple methods to guide the application of EVBS in different climate zones of China, especially for nearly zero-energy bu ildings (NZEBs). This study explored sensitivity and regressi on analysis to identify and prioritize the most influencing parameters for cooling demand and shading performance, then used regression models to predict the shading performance. A new method of MC-AHP was proposed and used to quanti fy the se nsitivity of the related factors, which was classified into three categories, on cooling demand and shading performance. The sensitivity analysis identifi ed meteorological para meters are the most significant parameters affecting the cooling demand with a comprehensive weight of 0.82. Followed by building parameters with a weight of 0.11 and shading parameters with a weight of 0.07. The key influential sub-factors of these three types of parameters are outdoor temperature and solar radiation; shape factor (SF) and south window-to-wall ratio (SWWR); shading orientation (SO) and slat angle (SA), respectively. In addition, regression models between the shading performance and the key influential sub-factors were established for NZEB s with EVBS in different climate regions of China. And error anal ysis verified the accuracy of these models, which reveals that these regression models can effectively and accurately predict the shading performance. The regression analysis results indicate that WWR, SF, and SA are linearly related to the shading performance of EVBS, and the shading performance is always linearly related to SA regardless of how the shading orientation, WWR and SF change. This study can help architects determine the priority order of the optimal design of different building parameters in decision-making stages, then guide the efficient application of EVBS in new and retrofitted NZEBs.