A two-stage deep learning-based hybrid model for daily wind speed forecasting
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....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024170570 |
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| author | Shahab S. Band Rasoul Ameri Sultan Noman Qasem Saeid Mehdizadeh Brij B. Gupta Hao-Ting Pai Danyal Shahmirzadi Ely Salwana Amir Mosavi |
| author_facet | Shahab S. Band Rasoul Ameri Sultan Noman Qasem Saeid Mehdizadeh Brij B. Gupta Hao-Ting Pai Danyal Shahmirzadi Ely Salwana Amir Mosavi |
| author_sort | Shahab S. Band |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a687ecc8d781435c89690b031d2e0df3 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-a687ecc8d781435c89690b031d2e0df32025-01-17T04:50:00ZengElsevierHeliyon2405-84402025-01-01111e41026A two-stage deep learning-based hybrid model for daily wind speed forecastingShahab S. Band0Rasoul Ameri1Sultan Noman Qasem2Saeid Mehdizadeh3Brij B. Gupta4Hao-Ting Pai5Danyal Shahmirzadi6Ely Salwana7Amir Mosavi8Future 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, TaiwanDepartment of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, TaiwanComputer 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, YemenWater Engineering Department, Urmia University, Urmia, IranDepartment 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. Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan.Department of Big Data Business Analytics, National Pingtung University, Pingtung, TaiwanGraduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Douliou, 64002, Yunlin, TaiwanInstitute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, MalaysiaJohn 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.http://www.sciencedirect.com/science/article/pii/S2405844024170570Wind speedForecastingGradient boostingLong short-term memoryMachine learningArtificial intelligence |
| spellingShingle | Shahab S. Band Rasoul Ameri Sultan Noman Qasem Saeid Mehdizadeh Brij B. Gupta Hao-Ting Pai Danyal Shahmirzadi Ely Salwana Amir Mosavi A two-stage deep learning-based hybrid model for daily wind speed forecasting Heliyon Wind speed Forecasting Gradient boosting Long short-term memory Machine learning Artificial intelligence |
| title | A two-stage deep learning-based hybrid model for daily wind speed forecasting |
| title_full | A two-stage deep learning-based hybrid model for daily wind speed forecasting |
| title_fullStr | A two-stage deep learning-based hybrid model for daily wind speed forecasting |
| title_full_unstemmed | A two-stage deep learning-based hybrid model for daily wind speed forecasting |
| title_short | A two-stage deep learning-based hybrid model for daily wind speed forecasting |
| title_sort | two stage deep learning based hybrid model for daily wind speed forecasting |
| topic | Wind speed Forecasting Gradient boosting Long short-term memory Machine learning Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024170570 |
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