PV driven air conditioning (PVAC) systems integrated with building integrated photovoltaics (BIPV) are critical for achieving nearly zero energy buildings (NZEBs). Building design parameters simultaneously affect both PV generation and building load demand, yet the complex interactions among these parameters and their collective impact on dynamic energy matching are not well understood. This oversight frequently leads to suboptimal system performance. This study proposes a dynamic energy matching evaluation and optimization framework for PVAC-BIPV systems, incorporating physical models of their energy interactions and employing a systematic, multi-parameter approach to optimize building design parameters in different climatic regions. The maximum relative error of the simulation model for buildings integrated with various BIPV types is 9.80%. Univariate analysis reveals a clear hierarchy of influence, identifying shape factor as the most dominant parameter, followed by window-to-wall ratio (WWR) and then orientation. More importantly, the interaction between shape factor and WWR was identified as the most critical synergistic pairing, governing both available PV area and cooling demand. Through individual adjustment, synergistic combinations, and full-parameter optimization, the system achieved maximum SS improvements of 33.58% in Guangzhou and 24.21% in Shanghai, yielding ultimate SS values of 76.26% and 83.84%, respectively. The final designs for each region are characterized by their optimal parameter sets (45°, 0.555, 0.2) in Shanghai and (60°, 0.555, 0.2) in Guangzhou. The framework has great significance for the climate-adaptive design of PVAC integrated with BIPV types to achieve nearly ZEBs targets.

The energy matching of PV driven air conditioners is influenced by building load demand and PV generation. Merely increasing energy performance of building or PV capacity separately may improve the energy balance on a large time resolution, the real-time energy mismatching problem is still serious. In this study, a coordinated optimization method of PV capacity, building design, and load flexibility is proposed for improving the real-time energy matching of PVAC system. Then, a methodology integrating data mining method (XG Boost) and parametric simulation was developed to identify the determinant parameters of PV system and building design, exploring feature importance and correlations. The results of XG Boost indicate that the PV capacity, shape factor, and SHGC are the most critical factors. Finally, based on the optimized building design, the PCM layer was applied to improve the real time energy matching. To achieve a goal of 90 % ZEP, the PCM capacity can be decreased by 50.4 % and 62.8 % in Guangzhou and Shanghai in the optimized building. Moreover, the PV capacity can be reduced by 23 % in Guangzhou. The findings of this study provide practical guidance for designing PVAC system coupling with building design and energy storage devices.