Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations

نویسندگان

چکیده

Offshore wind turbines (OWTs), in comparison to onshore turbines, are gaining popularity worldwide since they create a large amount of electrical power and have thus become more financially viable recent years. However, OWTs costly as vulnerable damage from extremely high-speed winds thereby affect operation maintenance (O&M) operations (e.g., vessel access, repair, downtime). Therefore, accurate weather forecasting helps optimise farm O&M operations, improve safety, reduce the risk for operators. Sequential data-driven models recently found application solving problem; however, their offshore through is still limited needs further investigation. This paper fills this gap by proposing three sequential techniques, namely, long short-term memory (LSTM), bidirectional LSTM (BiLSTM) gated recurrent units (GRU) long-term forecasting. The proposed techniques then compared summarise strength weaknesses these concerning Weather datasets (wind speed wave height) intermittent over different time scales reflect conditions. These (obtained FINO3 database) will be used study training validation purposes. results suggest that technique can generate realistic reliable forecasts term. It also stated it responds better seasonality forecasted expected results. validated calculated values statistical performance metrics uncertainty quantification.

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ژورنال

عنوان ژورنال: Energies

سال: 2022

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en15197233