Regular Article

Wind speed prediction for hybrid-based energy integration

¹ Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Mukhtar Basri Street No. 3, Medan 20238, Indonesia
² Department of Aviation, Politeknik Penerbangan Medan, Penerbangan Street No. 85, Medan 20131, Indonesia
³ Department of Electrical Engineering, Universitas Amir Hamzah, Pancing Pasar V Street, Medan Estate 20219, Indonesia
⁴ Department of Electrical Engineering, Politeknik Negeri Medan, Almamater Street No. 1, Medan 20155, Indonesia
⁵ Department of Electrical Engineering, Universitas Medan Area, H Agus Salim Siregar Street, Medan Tembung 20223, Indonesia
⁶ Department of Aviation, Politeknik Penerbangan Indonesia, Raya PLP Curug Street, Banten 15820, Indonesia

^* Corresponding author: suwarno@umsu.ac.id

Received: 18 September 2024
Accepted: 15 November 2024

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.