A two-stage deep learning-based hybrid model for daily wind speed forecasting
Shahab S. Band,
Rasoul Ameri,
Sultan Noman Qasem,
Saeid Mehdizadeh,
Brij B. Gupta,
Hao-Ting Pai,
Danyal Shahmirzadi,
Ely Salwana,
Amir Mosavi
Affiliations
Shahab S. Band
Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan; Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Rasoul Ameri
Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Sultan Noman Qasem
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, 6803, Yemen
Saeid Mehdizadeh
Water Engineering Department, Urmia University, Urmia, Iran
Brij B. Gupta
Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan; Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India; Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India; Corresponding author. John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.
Hao-Ting Pai
Department of Big Data Business Analytics, National Pingtung University, Pingtung, Taiwan
Danyal Shahmirzadi
Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Douliou, 64002, Yunlin, Taiwan
Ely Salwana
Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
Amir Mosavi
John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary; Corresponding author. John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.
Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques.
