Application of ERA5 and MENA Simulations to Predict Offshore Wind Energy Potential<strong> </strong>

Abstract

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near surface wind speeds to those of wind speed at turbine hub-height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 as a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plan and constructions.

Machine learning application in modelling marine and coastal phenomena: a critical review

“…Despite the inherent complexity of marine processes, supervised-based ML models have demonstrated benefits in understanding coastal phenomena, thereby finding extensive application in coastal engineering to drive innovative models and solve intricate problems (as summarized in Table 5). Supervised ML models have been employed to predict wave parameters like significant wave height and period, wave reflection and transmission coefficients (van Gent et al, 2007;Gandomi et al, 2020;Kuntoji et al, 2020), tide levels (Lee, 2004), ocean currents and wind files (James et al, 2018;Shamshirband et al, 2020), prediction of wind Characteristics under future Climate Change scenarios (Yeganeh-Bakhtiary et al, 2022), flood inundation using Gaussian process model (Donnelly et al, 2022) and breakwater stability number and wave overtopping discharge, among others. Various ML models, such as ANN and SVM, can be employed to do these predictions.…”

Investigating the Role of Bias Correction Methods and Climate Models on Water Budget of Büyük Menderes Basin

“…Currently, RCMs with 5 km resolution are more in use. However, datasets with this highest resolution are not available for regions such as the Middle East and North Africa (MENA) [5][6][7][8]. Therefore, for studying climate change impacts at finer scales, such as at the watershed scale, it is necessary to perform downscaling methods to produce local-scale, bias-corrected, and finerresolution climate datasets based on the outputs of GCMs and RCMs [9].…”

Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

“…Initially, by considering the wind and geographic information of the region, the wake effect is analyzed with the Jensen method [59,60]. Next, the objective function, which is the cost of total power generation [40,61,62], will be estimated. Finally, the objective function will be optimized using the particle swarm optimization algorithm.…”