Use of state-of-art machine learning technologies for forecasting offshore wind speed, wave and misalignment to improve wind turbine performance

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dc.contributor.authorSacie, Montserrat
dc.contributor.authorSantos, Matilde
dc.contributor.authorLópez, Rafael
dc.contributor.authorPandit, Ravi
dc.date.accessioned2022-07-25T13:27:54Z
dc.date.available2022-07-25T13:27:54Z
dc.date.issued2022-07-08
dc.description.abstractOne of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural fatigue and may cause them to lose efficiency. Thus, it is imperative to develop models to facilitate their deployment while maximizing energy production and ensuring the structure’s safety. This work applies machine learning (ML) techniques to obtain predictive models of the most relevant metocean variables involved. Specifically, wind speed, significant wave height, and the misalignment between wind and waves have been analyzed, pre-processed and modeled based on actual data. Linear regression (LR), support vector machines regression (SVR), Gaussian process regression (GPR) and neural network (NN)-based solutions have been applied and compared. The results show that Nonlinear autoregressive with an exogenous input neural network (NARX) is the best algorithm for both wind speed and misalignment forecasting in the time domain (72% accuracy) and GPR for wave height (90.85% accuracy). In conclusion, these models are vital to deploying and installing FOWTs and making them profitable.en_UK
dc.identifier.citationSacie M, Santos M, López R, Pandit R. (2022) Use of state-of-art machine learning technologies for forecasting offshore wind speed, wave and misalignment to improve wind turbine performance. Journal of Marine Science and Engineering, Volume 10, Issue 7, July 2022, Article number 938en_UK
dc.identifier.issn2077-1312
dc.identifier.urihttps://doi.org/10.3390/jmse10070938
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18224
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectWind energyen_UK
dc.subjectmachine learningen_UK
dc.subjectperformance monitoringen_UK
dc.subjectSCADA dataen_UK
dc.titleUse of state-of-art machine learning technologies for forecasting offshore wind speed, wave and misalignment to improve wind turbine performanceen_UK
dc.typeArticleen_UK

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