Data-driven weather forecasting modelsperformance comparison for improvingoffshore wind turbine availability andmaintenance

Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these togethercalled turbine weather datasets that are variable and intermittent over various time-scales and signify offshore weatherconditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavorable weather prevents repairand maintenance activities. This study proposes two data-driven models for long-term weather conditions forecasting to supportoperation and maintenance (O&M) decision-making process. These two data-driven approaches are long short-term memorynetwork, abbreviated as LSTM, and Markov chain. An LSTM is an artificial recurrent neural network, capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markovis another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on theevents that occurred before. The readily available weather FINO3 datasets are used to train and validate the performance ofthese models. A performance comparison between these weather forecasted models would be carried out to determine whichapproach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. Theentire study outlines the weakness and strength associated with proposed models in relations to offshore wind farms operationalactivitie