Citation: Al-Quraan, A.; Al-Mhairat, B.; Malkawi, A.M.A.; Radaideh, A.; Al-Masri, H.M.K. Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan. Sustainability 2023, 15, 3927. https:// doi.org/10.3390/su15053927 Academic Editor: Mohamed A. Mohamed Received: 3 January 2023 Revised: 3 February 2023 Accepted: 16 February 2023 Published: 21 February 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan Ayman Al-Quraan 1, * , Bashar Al-Mhairat 1 , Ahmad M. A. Malkawi 2 , Ashraf Radaideh 1 and Hussein M. K. Al-Masri 1 1 Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan 2 Mechatronics Engineering Department, The University of Jordan, Amman 11942, Jordan * Correspondence: aymanqran@yu.edu.jo or aymanqran@yahoo.com Abstract: The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R 2 ), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (E D ) and the total wind energy (E T ) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m 2 to nearly 1250 kWh/m 2 . In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern. Keywords: performance indicator; power density; probability distribution function; whale optimiza- tion algorithm; wind energy estimation; Gamma approach 1. Introduction Currently, renewable energy is a crucial and essential need to mitigate the hazards caused by fossil fuels. Although the global capacity of renewable energy in 2019 was 2588 GW, the percentage of electricity generation from renewable resources was about 27.3% versus 72.7% for traditional resources [1]. Therefore, all essential steps must be taken by governments worldwide to support these strategies. One of the most highly developed sectors in the field of renewable energy is the wind energy sector. In 2015, 63.8 GW of wind power capacity was installed in networks throughout the globe, recording the greatest yearly increase in the capacity of wind power generation. In 2019, the second largest yearly increase in the capacity of wind power generation was recorded with a value of 60 GW [1,2]. The climate of Jordan enhances the utilization of renewable energy sources. It contains various locations that are appealing for wind energy investment. However, Jordan is still 93% dependent on its energy from gas and oil imported from other countries. In 2018, Sustainability 2023, 15, 3927. https://doi.org/10.3390/su15053927 https://www.mdpi.com/journal/sustainability