Nature-Inspired Driven Deep-AI Algorithms for Wind Speed Prediction

Predicting wind energy production accurately is crucial for enhancing grid management and dispatching capacity. However, the inherent unpredictability of wind speed poses significant challenges to achieving high prediction accuracy.To address this challenge, this study introduces a novel pre-processing framework that leverages thirteen nature-inspired optimization algorithms to extract and combine Intrinsic Mode Functions (IMFs) of atmospheric and wind speed variables. The objective function ensures that the selected IMF combinations exhibit high correlation, enhancing their predictive relevance.The outputs of these algorithms are further refined using the proposed Optimal Search IMF (OAIMF) algorithm, which reduces redundancy and selects a minimal yet highly relevant set of IMF combinations for wind speed prediction.The methodology was validated through a case study conducted at the Climate, Energy, and Water Research Institute (CEWRI), NARC, Islamabad, Pakistan, leveraging real-world atmospheric data.Experimental results demonstrate that the proposed framework significantly outperforms direct prediction methods and state-of-the-art pre-processing techniques. For instance, the framework achieved an RMSE of 2.73 on an LSTM network and 3.86 on a GRU network, compared to RMSE values of 19.78 and 18.89, respectively, for direct prediction. Superior performance was also observed across MAE, MAPE, and R2 metrics.This study highlights the critical role of robust pre-processing in enhancing deep learning-based wind speed prediction. By integrating nature-inspired optimization with a novel IMF selection strategy, the proposed approach advances the state-of-the-art in renewable energy forecasting.