Multi-parameter interaction optimization for dynamic energy matching in PV driven air conditioning with different BIPV types

Abstract

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.