Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power one the most important green and resources. The estimation generation a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We optimized dendritic neural regression (DNR) prediction. A new variant seagull algorithm (SOA) search operators Aquila optimizer (AO). main idea apply AO as local traditional SOA, which boosts SOA’s capability. method, called SOAAO, employed train optimize DNR parameters. used four speed datasets assess performance presented time-series prediction model, DNR-SOAAO, indicators. also assessed quality SOAAO with extensive comparisons original versions SOA AO, well several other methods. achieved excellent results evaluation. For example, high R2 0.95, 0.96, 0.91 on datasets.