High-quality ground solar irradiance data is crucial for various solar energy applications, but it faces challenges in terms of accuracy and reliability due to data errors and environmental factors. This study, applied at the Green Energy Park meteorological station in Ben-Guerir during February and March 2023, evolves modeling algorithms using Python software to analyze solar radiation data and simulate it in order to enhance its quality. The algorithms developed involve visual inspection to detect and rectify errors, including time shifts, outliers and missing values. It also uses the Basic Surface Radiation Monitoring Network (BSRN) procedure, encompassing the Possible Physical Limits (PPL), Extremely Rare Limits (ERL), and consistency tests for Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) to identify and mitigate anomalies. Anomalies in the meteorological parameters, linked to data logger restarts, are addressed by interpreting them as median values. Cloud presence significantly affects solar irradiance data, increasing DHI and reducing DNI, while misalignment between pyranometers and shadow bands, along with instrument cleanliness and sensor dew, disrupt data quality.