Data‐driven models for short‐term ocean wave power forecasting
Abstract In order to integrate wave farms into the grid, the power from wave energy converters (WEC) must be forecasted. This study presents a novel data‐driven modelling (DDM) method to predict very short‐term (15 min–4 h) and short‐term (0–72 h) power generation from a WEC. The model is tested usi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-07-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.12157 |
| _version_ | 1797995356636577792 |
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| author | Chenhua Ni |
| author_facet | Chenhua Ni |
| author_sort | Chenhua Ni |
| collection | DOAJ |
| description | Abstract In order to integrate wave farms into the grid, the power from wave energy converters (WEC) must be forecasted. This study presents a novel data‐driven modelling (DDM) method to predict very short‐term (15 min–4 h) and short‐term (0–72 h) power generation from a WEC. The model is tested using data from an oscillating body converter. Several other methods are tested as well. These include support vector machines (SVM), neural networks (NN), and recurrent neural networks (RNN). Of these, the best is the long‐short‐term memory (LSTM) network, which is trained and updated on observed values. The experiments demonstrate both the SVM and NN forecast well. However, the proposed deep learning models predict them more accurately. The models work well over short horizons. At horizons longer than three days, accuracy deteriorates, and the models cannot fit the data well. |
| first_indexed | 2024-04-11T09:59:09Z |
| format | Article |
| id | doaj.art-23a9489f0a3543cf9ac783b4790344d0 |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-11T09:59:09Z |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-23a9489f0a3543cf9ac783b4790344d02022-12-22T04:30:27ZengWileyIET Renewable Power Generation1752-14161752-14242021-07-0115102228223610.1049/rpg2.12157Data‐driven models for short‐term ocean wave power forecastingChenhua Ni0National Ocean Technology Center Tianjin ChinaAbstract In order to integrate wave farms into the grid, the power from wave energy converters (WEC) must be forecasted. This study presents a novel data‐driven modelling (DDM) method to predict very short‐term (15 min–4 h) and short‐term (0–72 h) power generation from a WEC. The model is tested using data from an oscillating body converter. Several other methods are tested as well. These include support vector machines (SVM), neural networks (NN), and recurrent neural networks (RNN). Of these, the best is the long‐short‐term memory (LSTM) network, which is trained and updated on observed values. The experiments demonstrate both the SVM and NN forecast well. However, the proposed deep learning models predict them more accurately. The models work well over short horizons. At horizons longer than three days, accuracy deteriorates, and the models cannot fit the data well.https://doi.org/10.1049/rpg2.12157Tidal and flow energySurface waves, tides, and sea levelInstrumentation and techniques for geophysical, hydrospheric and lower atmosphere researchWave power |
| spellingShingle | Chenhua Ni Data‐driven models for short‐term ocean wave power forecasting IET Renewable Power Generation Tidal and flow energy Surface waves, tides, and sea level Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Wave power |
| title | Data‐driven models for short‐term ocean wave power forecasting |
| title_full | Data‐driven models for short‐term ocean wave power forecasting |
| title_fullStr | Data‐driven models for short‐term ocean wave power forecasting |
| title_full_unstemmed | Data‐driven models for short‐term ocean wave power forecasting |
| title_short | Data‐driven models for short‐term ocean wave power forecasting |
| title_sort | data driven models for short term ocean wave power forecasting |
| topic | Tidal and flow energy Surface waves, tides, and sea level Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Wave power |
| url | https://doi.org/10.1049/rpg2.12157 |
| work_keys_str_mv | AT chenhuani datadrivenmodelsforshorttermoceanwavepowerforecasting |