Wind speed prediction for hybrid-based energy integration

Abstract

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 and 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 (LSSVM) model to obtain parts of the system with new variants. This study proposes a hybrid model for short-term Water Supply Footprint (WSF) that takes into account the suitability of the LSSVM model for average data size and computational resources with an improved Quantum-Behaved Particle Swarm Optimization (QPSO) algorithm to optimize its parameters, with an Long Short-Term Memory (LSTM) network to model irregular sequences, and the advantages of the Sequential VMD (SVMD) 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 Root Mean Squared Error (RMSE) of 0.703, Mean Absolute Error (MAE) of 0.512, mean absolute percentage error (MAPE) of 5.9%, R2 of 0.796, and a correlation coefficient of 0.892.