Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations
Offshore wind turbines (OWTs), in comparison to onshore wind turbines, are gaining popularity worldwide since they create a large amount of electrical power and have thus become more financially viable in recent years. However, OWTs are costly as they are vulnerable to damage from extremely high-spe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/19/7233 |
| _version_ | 1797479434261889024 |
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| author | Ravi Pandit Davide Astolfi Anh Minh Tang David Infield |
| author_facet | Ravi Pandit Davide Astolfi Anh Minh Tang David Infield |
| author_sort | Ravi Pandit |
| collection | DOAJ |
| description | Offshore wind turbines (OWTs), in comparison to onshore wind turbines, are gaining popularity worldwide since they create a large amount of electrical power and have thus become more financially viable in recent years. However, OWTs are costly as they are vulnerable to damage from extremely high-speed winds and thereby affect operation and maintenance (O&M) operations (e.g., vessel access, repair, and downtime). Therefore, accurate weather forecasting helps to optimise wind farm O&M operations, improve safety, and reduce the risk for wind farm operators. Sequential data-driven models recently found application in solving the wind turbines problem; however, their application to improve offshore operation and maintenance through weather forecasting is still limited and needs further investigation. This paper fills this gap by proposing three sequential data-driven techniques, namely, long short-term memory (LSTM), bidirectional LSTM (BiLSTM) and gated recurrent units (GRU) for long-term weather forecasting. The proposed techniques are then compared to summarise the strength and weaknesses of these models concerning long-term weather forecasting. Weather datasets (wind speed and wave height) are intermittent over different time scales and reflect offshore weather conditions. These datasets (obtained from the FINO3 database) will be used in this study for training and validation purposes. The study results suggest that the proposed technique can generate more realistic and reliable weather forecasts in the long term. It can also be stated that it responds better to seasonality and forecasted expected results. This is further validated by the calculated values of statistical performance metrics and uncertainty quantification. |
| first_indexed | 2024-03-09T21:46:40Z |
| format | Article |
| id | doaj.art-37ca858aef494cb397fdcc8330f248d3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:46:40Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-37ca858aef494cb397fdcc8330f248d32023-11-23T20:15:26ZengMDPI AGEnergies1996-10732022-10-011519723310.3390/en15197233Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance OperationsRavi Pandit0Davide Astolfi1Anh Minh Tang2David Infield3Centre for Life-Cycle Engineering and Management, Cranfield University, Bedford MK43 0AL, UKDepartment of Engineering, University of Perugia, Via G. Duranti, 06125 Perugia, ItalyÉcole des Ponts ParisTech (ENPC), Ministry for the Ecological Transition, 77420 Paris, FranceElectronics and Electrical Engineering Department, University of Strathclyde, Glasgow MK43 0AL, UKOffshore wind turbines (OWTs), in comparison to onshore wind turbines, are gaining popularity worldwide since they create a large amount of electrical power and have thus become more financially viable in recent years. However, OWTs are costly as they are vulnerable to damage from extremely high-speed winds and thereby affect operation and maintenance (O&M) operations (e.g., vessel access, repair, and downtime). Therefore, accurate weather forecasting helps to optimise wind farm O&M operations, improve safety, and reduce the risk for wind farm operators. Sequential data-driven models recently found application in solving the wind turbines problem; however, their application to improve offshore operation and maintenance through weather forecasting is still limited and needs further investigation. This paper fills this gap by proposing three sequential data-driven techniques, namely, long short-term memory (LSTM), bidirectional LSTM (BiLSTM) and gated recurrent units (GRU) for long-term weather forecasting. The proposed techniques are then compared to summarise the strength and weaknesses of these models concerning long-term weather forecasting. Weather datasets (wind speed and wave height) are intermittent over different time scales and reflect offshore weather conditions. These datasets (obtained from the FINO3 database) will be used in this study for training and validation purposes. The study results suggest that the proposed technique can generate more realistic and reliable weather forecasts in the long term. It can also be stated that it responds better to seasonality and forecasted expected results. This is further validated by the calculated values of statistical performance metrics and uncertainty quantification.https://www.mdpi.com/1996-1073/15/19/7233wind turbineoffshore windweather forecastingdeep learningmachine learning |
| spellingShingle | Ravi Pandit Davide Astolfi Anh Minh Tang David Infield Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations Energies wind turbine offshore wind weather forecasting deep learning machine learning |
| title | Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations |
| title_full | Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations |
| title_fullStr | Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations |
| title_full_unstemmed | Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations |
| title_short | Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations |
| title_sort | sequential data driven long term weather forecasting models performance comparison for improving offshore operation and maintenance operations |
| topic | wind turbine offshore wind weather forecasting deep learning machine learning |
| url | https://www.mdpi.com/1996-1073/15/19/7233 |
| work_keys_str_mv | AT ravipandit sequentialdatadrivenlongtermweatherforecastingmodelsperformancecomparisonforimprovingoffshoreoperationandmaintenanceoperations AT davideastolfi sequentialdatadrivenlongtermweatherforecastingmodelsperformancecomparisonforimprovingoffshoreoperationandmaintenanceoperations AT anhminhtang sequentialdatadrivenlongtermweatherforecastingmodelsperformancecomparisonforimprovingoffshoreoperationandmaintenanceoperations AT davidinfield sequentialdatadrivenlongtermweatherforecastingmodelsperformancecomparisonforimprovingoffshoreoperationandmaintenanceoperations |