Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output
This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/13/3532 |
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| author | Mohammad Barooni Deniz Velioglu Sogut Parviz Sedigh Masoumeh Bahrami |
| author_facet | Mohammad Barooni Deniz Velioglu Sogut Parviz Sedigh Masoumeh Bahrami |
| author_sort | Mohammad Barooni |
| collection | DOAJ |
| description | This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. This model addresses computational complexity and lengthy processing times of conventional models, offering adaptability, scalability, and efficient handling of nonlinear dynamics. The results for predicting the generator power of a spar-type floating offshore wind turbine (FOWT) in a multivariable parallel time-series dataset using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model showed promising outcomes, offering valuable insights into the model’s performance and potential applications. Its ability to capture a comprehensive range of load case scenarios—from mild to severe—through the integration of multiple relevant features significantly enhances the model’s robustness and applicability in realistic offshore environments. The research demonstrates the potential of deep learning methods in advancing renewable energy technology, specifically in optimizing turbine efficiency, anticipating maintenance needs, and integrating wind power into energy grids. |
| format | Article |
| id | doaj-art-4d91aef2b2284f6ba408a5ddeed2fb44 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-4d91aef2b2284f6ba408a5ddeed2fb442025-08-20T02:35:47ZengMDPI AGEnergies1996-10732025-07-011813353210.3390/en18133532Novel Hybrid Deep Learning Model for Forecasting FOWT Power OutputMohammad Barooni0Deniz Velioglu Sogut1Parviz Sedigh2Masoumeh Bahrami3Ocean Engineering and Marine Science, Florida Institute of Technology, Melbourne, FL 32901, USAOcean Engineering and Marine Science, Florida Institute of Technology, Melbourne, FL 32901, USAMechanical Engineering, University of New Hampshire, Durham, NH 03824, USAElectrical and Computer Engineering, University of New Hampshire, Durham, NH 03824, USAThis study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. This model addresses computational complexity and lengthy processing times of conventional models, offering adaptability, scalability, and efficient handling of nonlinear dynamics. The results for predicting the generator power of a spar-type floating offshore wind turbine (FOWT) in a multivariable parallel time-series dataset using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model showed promising outcomes, offering valuable insights into the model’s performance and potential applications. Its ability to capture a comprehensive range of load case scenarios—from mild to severe—through the integration of multiple relevant features significantly enhances the model’s robustness and applicability in realistic offshore environments. The research demonstrates the potential of deep learning methods in advancing renewable energy technology, specifically in optimizing turbine efficiency, anticipating maintenance needs, and integrating wind power into energy grids.https://www.mdpi.com/1996-1073/18/13/3532deep learningoffshore wind turbineneural networksCNN-LSTM |
| spellingShingle | Mohammad Barooni Deniz Velioglu Sogut Parviz Sedigh Masoumeh Bahrami Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output Energies deep learning offshore wind turbine neural networks CNN-LSTM |
| title | Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output |
| title_full | Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output |
| title_fullStr | Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output |
| title_full_unstemmed | Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output |
| title_short | Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output |
| title_sort | novel hybrid deep learning model for forecasting fowt power output |
| topic | deep learning offshore wind turbine neural networks CNN-LSTM |
| url | https://www.mdpi.com/1996-1073/18/13/3532 |
| work_keys_str_mv | AT mohammadbarooni novelhybriddeeplearningmodelforforecastingfowtpoweroutput AT denizvelioglusogut novelhybriddeeplearningmodelforforecastingfowtpoweroutput AT parvizsedigh novelhybriddeeplearningmodelforforecastingfowtpoweroutput AT masoumehbahrami novelhybriddeeplearningmodelforforecastingfowtpoweroutput |