Accurately determining single-diode model parameters yields essential insights into the photovoltaic (PV) device performance and behavior. Analytical methods for extracting these parameters often rely on mathematical assumptions typically valid under controlled indoor conditions. Applying these methods to PV modules in the field introduces complexities due to varying environmental conditions and module technologies, leading to divergencies between parameters extracted under outdoor and indoor conditions. This study closes the gap in analyzing the retrieved parameters under intricate outdoor conditions by differentiating between all-, clear-, and cloudy-sky conditions and varying irradiances for different PV technologies. We examine three methods over a year of outdoor I-V curves from Al-BSF, HIT, and a-Si/µc-Si PV modules in Lima, Peru, a low-latitude site. The findings represent the first mid-term study by the country’s premier laboratory uniquely equipped for diverse outdoor PV module characterization. We evaluate the accuracy of each method using the Normalized Root Mean Square Error (NRMSE) by comparing experimental against simulated I-V curves derived from the extracted parameters. Our findings reveal that the parameters for the Al-BSF and HIT modules under all-sky conditions align with reported outdoor trends for varying irradiances, while under clear skies, they correspond with indoor trends. In terms of accuracy, the methods by Phang et al. and de Blas et al. consistently achieve an average NRMSE below 1 % across all PV module types under all-sky conditions. However, when differentiating between sky conditions, the NRMSE values for the Al-BSF and HIT modules are notably lower under clear sky conditions at any irradiance level, preserving a mean value below 0.6 %, unlike the a-Si/µc-Si PV technology, which shows more consistent NRMSE values across all sky conditions and most irradiance levels and in average above 0.7 %. These results demonstrate that selecting sky conditions based on the evaluated PV technology is beneficial for enhanced accuracy in outdoor parameter extraction.