(untitled) (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2026)
Accurate wind data is important for clean energy planning. However, many weather datasets have missing values due to sensor issues. This study explores how missing data affects Weibull parameter estimation and compares four methods: the empirical method of Justus (EMJ), maximum likelihood estimation (MLE), moment method (MM), and least squares method (LSM). Five years of data (2020–2024) from 14 stations in Southern Thailand were used. Wind speeds ranged from 0.35 to 2.18 m/s, with a peak of 32.98 m/s. Data completeness varied from 27.61% to 84.14%. Model accuracy was tested using MAPE, RMSE, Chi-square, and R². EMJ and MM gave strong results with complete data. MLE worked best with 50–80% completeness. LSM showed high R² but overfitted with missing data. MM was the most reliable overall. These findings support better wind modeling in areas with limited data, helping clean energy development in Southern Thailand.