Hybrid Deep Learning Technique for Optimal Wind Mill Speed Estimation

Deep learning and windmill speed estimation may have seen further advancements and new challenges since then. Recent Techniques include Hybrid Recurrent Neural Networks (RNNs) with Attention Mechanisms, Transfer Learning, Physics-Informed Deep Learning, and Ensemble Learning with Neural Networks. Challenges include Data Quality and Quantity, Non-Stationarity, Complex Interactions, Model Interpretability, and Resource Constraints. Addressing these challenges is essential to create accurate, reliable, and safe windmill speed estimation systems that can optimize the performance of wind turbines and contribute to sustainable energy production. The goal of this research is to create AI-based wind speed estimating algorithms to assist utility planners and workers in balancing supply and petition in the electric grid, addressing the complicated operation of wind energy. Using Extended Empirical Mode Decomposition (EEMD) and Deep Boltzmann Machine (DBM) networks, a hybrid deep learning system improves wind speed forecast accuracy. DBM network extracted complex properties and verified model using Indian wind farm data. The hybrid EEMD-DBM method improved the MAE, RMSE, and MAPE indices by 77.20 %, 58.97 %, and 77.33 %, respectively. The suggested model outperforms the Back Propagation Neural Researchers should optimize the technique by combining wind path with input time-series and increasing hidden layers in the network model.