{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:06:44Z","timestamp":1790892404309,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deputyship for Research &amp; Innovation, Ministry of Education in Saudi Arabia","award":["RI-44-0649"],"award-info":[{"award-number":["RI-44-0649"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson\u2019s correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results.<\/jats:p>","DOI":"10.3390\/biomimetics8030321","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T01:58:38Z","timestamp":1689904718000},"page":"321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting"],"prefix":"10.3390","volume":"8","author":[{"given":"Faten Khalid","family":"Karim","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doaa Sami","family":"Khafaga","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marwa M.","family":"Eid","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. K.","family":"Towfek","sequence":"additional","affiliation":[{"name":"Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA"},{"name":"Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-5267","authenticated-orcid":false,"given":"Hend K.","family":"Alkahtani","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Valdivia-Bautista, S.M., Dom\u00ednguez-Navarro, J.A., P\u00e9rez-Cisneros, M., Vega-G\u00f3mez, C.J., and Castillo-T\u00e9llez, B. (2023). Artificial Intelligence in Wind Speed Forecasting: A Review. Energies, 16.","DOI":"10.3390\/en16052457"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ibrahim, A., El-kenawy, E.S.M., Kabeel, A.E., Karim, F.K., Eid, M.M., Abdelhamid, A.A., Ward, S.A., El-Said, E.M.S., El-Said, M., and Khafaga, D.S. (2023). Al-Biruni Earth Radius Optimization Based Algorithm for Improving Prediction of Hybrid Solar Desalination System. 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