Open and reproducible estimation of PV single-diode parameters from datasheet data

Photovoltaic (PV) module datasheets typically provide only five key electrical parameters under Standard Test Conditions: open-circuit voltage, short-circuit current, voltage and current at maximum power, and nominal peak power. Although these data are routinely available, they are not sufficient to derive the complete Single-Diode Model (SDM) representation required for accurate performance simulations. This study addresses this limitation by proposing a fully open-source and reproducible methodology to extract the full set of SDM parameters using only manufacturer datasheet key-points, without requiring measured I-V curves. The approach employs a Genetic Algorithm to minimize a composite relative-error index over the datasheet points, allowing all five SDM parameters to vary freely within physical constraints. The complete implementation and all associated scripts are publicly released under a permissive open-source license, ensuring unrestricted access and transparency. Validation is performed on an extensive dataset of 20,000 commercial PV modules from public repositories (SAM/CEC). Results demonstrate high repeatability (median RMSE ≈ 0.003), computational efficiency (median runtime < 6 s), and excellent agreement with reference data, confirming that the proposed framework provides a transparent and reproducible tool for parameter extraction at STC, suitable for both academic research and large-scale industrial database analyses. • Fully open-source implementation for parameters extraction at standard conditions. • Unprecedented extended validation scale over 20,000 commercial modules. • Use of only standard datasheet values without experimental I-V curves. • Complete algorithmic transparency and reproducibility issuing original code. • Good accuracy across diverse PV technologies: median RMSE of 0.3%.