Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind through an improved al-Biruni Earth radius (BER) metaheuristic algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined optimized achieve superior accuracy prediction. proposed denoted by BERSFS used ensemble model’s feature selection boost prediction accuracy. In the experiments, first scenario covers binary algorithm’s capabilities for dataset under test, while second demonstrates regression capabilities. investigated compared state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, whale BERSFS-based model also basic models long short-term memory, bidirectional gated recurrent unit, k-nearest neighbor model. statistical investigation utilized Wilcoxon’s rank-sum analysis variance tests investigate robustness created achieved results confirm effectiveness superiority approach forecasting.