An integrated thermo-electric performance evaluation and design decision-making framework for closed-air-cavity sandwich-type wall-integrated photovoltaics

Abstract

Closed-air-cavity sandwich-type wall-integrated photovoltaics (SWIPV) provide power-generation and building-envelope functions, but their complex electrical and thermal behavior challenges the simultaneous calculation and evaluation of thermo-electric performance and engineering design. By analyzing the different operating mechanisms during daytime and nighttime, this study proposes cumulative electricity generation (E_SWIPV) and the period-averaged operational apparent equivalent heat-transfer coefficient (K_SWIPV) to characterize thermo-electric performance, respectively, and develops a coupled calculation model. Furthermore, by incorporating the comprehensive performance index (CI) and performance balance index (BI), an integrated evaluation and design decision-making framework is established. Experiments were conducted to verify the accuracy of the model. The response regularity of thermo-electric performance and the corresponding design decisions were analyzed by constructing combined conditions involving nine representative sites in 5 Chinese climate zones, 3 facade orientations, and various air layer thicknesses. Results show that the maximum model error is below 6.0%; differences in E_SWIPV among climate zones are mainly determined by facade irradiation, whereas K_SWIPV is jointly affected by facade irradiation, air temperature, and seasonal distribution; nighttime Scenario 2 contributes 31.8% and 22.0% to K_SWIPV in Guangzhou and Nanchang, respectively; E_SWIPV and K_SWIPV tend to stabilize when air layer thickness exceeds 300 mm; climate and air layer thickness are the dominant factors affecting E_SWIPV and K_SWIPV, respectively; under the thickness-constrained scenario, the South-200 mm scheme is recommended for 8 sites except Guiyang under all 5 weighting coefficients. The framework can be transferred to other regions by inputting candidate conditions, local meteorological data, and design preferences.