Sequential data-driven long-term weather forecasting models’ performance comparison for improving offshore operation and maintenance operations

dc.contributor.authorPandit, Ravi
dc.contributor.authorAstolfi, Davide
dc.contributor.authorTang, Anh Minh
dc.contributor.authorInfield, David
dc.date.accessioned2022-10-05T14:32:39Z
dc.date.available2022-10-05T14:32:39Z
dc.date.issued2022-10-01
dc.description.abstractOffshore 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.en_UK
dc.identifier.citationPandit R, Astolfi D, Tang AM, Infield D. (2022) Sequential data-driven long-term weather forecasting models’ performance comparison for improving offshore operation and maintenance operations. Energies, Volume 15, Issue 19, October 2022, Article number 7233en_UK
dc.identifier.issn1996-1073
dc.identifier.urihttps://doi.org/10.3390/en15197233
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18526
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectwind turbineen_UK
dc.subjectweather forecastingen_UK
dc.subjectdeep learningen_UK
dc.subjectbig data computationen_UK
dc.titleSequential data-driven long-term weather forecasting models’ performance comparison for improving offshore operation and maintenance operationsen_UK
dc.typeArticleen_UK

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