Wind speed multistep forecasting model using a hybrid decomposition technique and a selfish herd optimizer-based deep neural network

In this manuscript, wind speed multistep forecasting model using a hybrid decomposition technique and a selfish herd optimizer-based deep neural network is proposed. The reliability and hygiene standards of wind energy are obtaining large stake. Indeed, it is difficult to ascertain a scientific and robust forecasting method due to the variability and the wind speed intervention. To wind farm operational scheduling, accurate and consistent prediction is crucial. Therefore, the wind speed array usually has dynamic characteristics including nonlinearity and variability, rendering the estimation of wind energy exceptionally challenging. The proposed hybrid decomposition technique incorporates the multivariate empirical mode decomposition (MEMD) with the specific enhanced empirical wavelet transform and is primarily utilized to progressively decompose MEMD’s high-intrinsic mode functions (IMFs). Then, strengthened DNN is widely used for the forecasting of all decomposed IMFs, so the components are using selfish herd optimizer algorithm. The data from Tamil Nadu region for certain coastal and hilly areas are used for multiforecasting to ascertain the predicting potential of the proposed method. The experimental outcomes demonstrate that the hypothesized model executes substantially better in the one five-step wind speed predicting than all other perceived models, suggesting that the proposed prototype is well suited to standardized multistep wind speed prediction.