Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System
The combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/24/9484 |
| _version_ | 1797459636562952192 |
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| author | Mahsa Dehghan Manshadi Milad Mousavi M. Soltani Amir Mosavi Levente Kovacs |
| author_facet | Mahsa Dehghan Manshadi Milad Mousavi M. Soltani Amir Mosavi Levente Kovacs |
| author_sort | Mahsa Dehghan Manshadi |
| collection | DOAJ |
| description | The combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for these sites. For this purpose, this research presents the feasibility of installing a combined hybrid site in the desired coastal location by predicting the net produced power due to the environmental parameters. For combining these two systems, an optimized array includes ten turbines and ten wave energy converters. The mathematical equations of the net force on the two introduced systems and the produced power of the wind turbines are proposed. The turbines’ maximum forces are 4 kN, and for the wave energy converters are 6 kN, respectively. Furthermore, the comparison is conducted in order to find the optimum system. The comparison shows that the most effective system of desired environmental condition is introduced. A number of machine learning and deep learning methods are used to predict key parameters after collecting the dataset. Moreover, a comparative analysis is conducted to find a suitable model. The models’ performance has been well studied through generating the confusion matrix and the receiver operating characteristic (ROC) curve of the hybrid site. The deep learning model outperformed other models, with an approximate accuracy of 0.96. |
| first_indexed | 2024-03-09T16:54:12Z |
| format | Article |
| id | doaj.art-e09361081ad94b18bbfa8ea1f8efbc2b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:54:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e09361081ad94b18bbfa8ea1f8efbc2b2023-11-24T14:37:41ZengMDPI AGEnergies1996-10732022-12-011524948410.3390/en15249484Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy SystemMahsa Dehghan Manshadi0Milad Mousavi1M. Soltani2Amir Mosavi3Levente Kovacs4Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, IranDepartment of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, IranDepartment of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, IranGerman Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyBiomatics and Applied Artificial Intelligence Institution, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, HungaryThe combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for these sites. For this purpose, this research presents the feasibility of installing a combined hybrid site in the desired coastal location by predicting the net produced power due to the environmental parameters. For combining these two systems, an optimized array includes ten turbines and ten wave energy converters. The mathematical equations of the net force on the two introduced systems and the produced power of the wind turbines are proposed. The turbines’ maximum forces are 4 kN, and for the wave energy converters are 6 kN, respectively. Furthermore, the comparison is conducted in order to find the optimum system. The comparison shows that the most effective system of desired environmental condition is introduced. A number of machine learning and deep learning methods are used to predict key parameters after collecting the dataset. Moreover, a comparative analysis is conducted to find a suitable model. The models’ performance has been well studied through generating the confusion matrix and the receiver operating characteristic (ROC) curve of the hybrid site. The deep learning model outperformed other models, with an approximate accuracy of 0.96.https://www.mdpi.com/1996-1073/15/24/9484renewable energyartificial intelligencemachine learningcomparative analysiswind turbineenergy |
| spellingShingle | Mahsa Dehghan Manshadi Milad Mousavi M. Soltani Amir Mosavi Levente Kovacs Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System Energies renewable energy artificial intelligence machine learning comparative analysis wind turbine energy |
| title | Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System |
| title_full | Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System |
| title_fullStr | Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System |
| title_full_unstemmed | Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System |
| title_short | Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System |
| title_sort | deep learning for modeling an offshore hybrid wind wave energy system |
| topic | renewable energy artificial intelligence machine learning comparative analysis wind turbine energy |
| url | https://www.mdpi.com/1996-1073/15/24/9484 |
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