Machine learning applications for wind resource mapping in Ajman, UAE towards sustainable energy solutions

Abstract

Accurate site-specific Sustainable Wind Resource Assessment (SWRA) remains a critical contemporarysustainable wind power development issue. Especially in regions like the Emirate of Ajman and theUnited 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 requiresextensive error correction for reliable application in SWRA.This research develops a comprehensive methodology for site-specific SWRA in the Emirate of Ajman throughfive integrated objectives: developing machine learning (ML)-based error correction methodology for NASAsatellite wind data, determining site-specific surface parameters, predicting future wind speed trends using ARIMAmodelling, analysing wind potential variations, and creating GIS-based wind resource maps. A systematic mixed-methodsapproach was used, integrating multiple ML algorithms (Random Forest, Support Vector Machine, Gradient Boosting) for NASAwind 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 strategicallylocated onshore monitoring stations validated the methodology and established site-specific correction factors across Ajman'sdiverse terrain. Results showed clear spatial and temporal variations in wind resources, with annual wind speeds rangingfrom 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 speedsreaching 5.69-6.16 m/s at 100m height. The Random Forest model achieved the highest accuracy (R² = 0.5772) insatellite data correction. Surface roughness length varied from 0.0002 (offshore) to 0.50 (urban areas), while air densityranged 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 theoreticaladvances in SWRA. While supporting the feasibility of hybrid wind-solar systems and offshore installations, the findingsalign with the UAE's Net Zero 2050 strategy and establish a systematic approach that other regions can follow to improvesatellite-derived wind speed data accuracy