Wind speed prediction for hybrid based energy integration

Wind energy has been widely explored and utilized as a renewable energy source. The integration of wind energy with other energy sources has been going well. To strengthen the current energy. However, this paper only discusses wind speed prediction in renewable energy by integrating hybrid-based energy. Combination of systems with the Successive model Variational Mode Decomposition uses the Least Squares Support Vector Machines model to obtain parts of the system with new variants. This study proposes a hybrid model for short-term WSF that takes into account the suitability of the LSSVM model for average data size and computational resources with an improved QPSO algorithm to optimize its parameters, with an LSTM network to model irregular sequences, and the advantages of the SVMD decomposition algorithm. This is to produce the predicted intrinsic mode and the error sequence is taken as the predicted final output wind speed result. For wind speed prediction, the assets in the proposed model obtained an RMSE of 0.703, MAE of 0.512, MAPE of 5.9%, R2 of 0.796, and a correlation coefficient of 0.892.