Abstract
Accurate site-specific Sustainable Wind Resource Assessment (SWRA) remains a critical contemporary
sustainable wind power development issue. Especially in regions like the Emirate of Ajman and the
United Arab Emirates (UAE), site-specific wind data collection faces high challenges and constraints.
Mainly due to the excessively high costs of measuring wind speeds at wind turbine hub heights level,
leading to dependence on publicly available NASA satellite data or any other freely available wind data that requires
extensive error correction for reliable application in SWRA.
This research develops a comprehensive methodology for site-specific SWRA in the Emirate of Ajman through
five integrated objectives: developing machine learning (ML)-based error correction methodology for NASA
satellite wind data, determining site-specific surface parameters, predicting future wind speed trends using ARIMA
modelling, analysing wind potential variations, and creating GIS-based wind resource maps. A systematic mixed-methods
approach was used, integrating multiple ML algorithms (Random Forest, Support Vector Machine, Gradient Boosting) for NASA
wind speed data correction, determination of site-specific parameters (wind shear coefficients, roughness length, air density),
statistical analysis of wind patterns, and GIS-based wind resource mapping. Ground-based measurements from strategically
located onshore monitoring stations validated the methodology and established site-specific correction factors across Ajman's
diverse terrain. Results showed clear spatial and temporal variations in wind resources, with annual wind speeds ranging
from 3.33- 3.74 m/s at 50m to 4.75-5.2 m/s at 100m height. Spring emerged as the optimal season, with wind speeds
reaching 5.69-6.16 m/s at 100m height. The Random Forest model achieved the highest accuracy (R² = 0.5772) in
satellite data correction. Surface roughness length varied from 0.0002 (offshore) to 0.50 (urban areas), while air density
ranged between 1.146-1.166 kg/m³. Offshore locations showed higher wind power density, reaching 126.12 W/m².
This study establishes Ajman's first validated, GIS integrated SWRA methodology, contributing to practical and theoretical
advances in SWRA. While supporting the feasibility of hybrid wind-solar systems and offshore installations, the findings
align with the UAE's Net Zero 2050 strategy and establish a systematic approach that other regions can follow to improve
satellite-derived wind speed data accuracy.