(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Photovoltaic module reliability depends on the long-term mechanical durability of polymeric backsheets that provide electrical insulation, moisture protection, and structural support. Accurate prediction of backsheet degradation is therefore essential for reliability-centered operation, maintenance scheduling, and lifecycle management of photovoltaic systems. This study develops a Generalized Additive Model (GAM) with penalized thin-plate regression splines and ridge regularization to predict tensile strength degradation under multi-stressor field conditions, benchmarked against ordinary least squares (OLS), weighted least squares (WLS), and Random Forest (RF) regression using 511 consecutive daily field observations of a PET/PET/EVA multilayer photovoltaic backsheet. The GAM achieves in-sample R² = 0.998 and RMSE = 0.8 MPa, compared with R² = 0.925 and RMSE = 4.69 MPa for OLS, and R² = 0.92 and RMSE = 4.84 MPa for WLS. RF achieves R² = 1.0 and RMSE = 0.2 MPa in-sample but captures no physically interpretable structure beyond UV dominance. Residual variance scales with cumulative UV dose at an exponent of α = 0.592 (SE = 0.105, p < 0.001). A biphasic degradation regime is identified, with tensile strength declining at 0.18 MPa/(MJ/m²) below a UV threshold of 196.5 MJ/m², and accelerating to 0.574 MPa/(MJ/m²) above it, a factor of 3.19. The GAM framework provides a physically interpretable and statistically defensible tool for forecasting photovoltaic backsheet degradation trajectories, with direct implications for predictive maintenance scheduling, module replacement planning, and reliability certification of photovoltaic systems.