Uncertain accessibility estimation method for offshore wind farm based on multi-step probabilistic wave forecasting

Hao Zhang, Jie Yan, Shuang Han, Li Li, Yongqian Liu, David Infield

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
14 Downloads (Pure)


Accessibility estimation is significant to the offshore wind farm operation and maintenance (O&M) due to the extremely limited weather window and its sensitive effects on O&M tasks. Wave forecasting can be one solution to help maintenance decision-making. However, the uncertain and dynamic properties of wave forecasts are seldom considered in the accessibility estimation process. This paper presents an uncertain accessibility estimation method based on a multi-step probabilistic wave height forecasting (MPWHF) model and Monte Carlo simulation. Firstly, an MPWHF model is proposed using the wavelet decomposition and the sequence to sequence(Seq2Seq) network with quantile outputs. Secondly, the O&M missions are randomly given a start time and simulated in the O&M flow chart by the Monte Carlo method. Finally, several access indexes, including accessibility probability, delay time, and delay probability, are evaluated based on the simulation results. Verification of the proposed MPWHF model and uncertain accessibility estimation is based on 7-year observation data of a buoy station. The results show that the MPWHF model outperforms other counterparts and the probability of offshore accessibility is nonlinearly dependent on the weather limits and the O&M required time.
Original languageEnglish
Pages (from-to)2944–2955
Number of pages12
JournalIET Renewable Power Generation
Issue number13
Early online date1 Jul 2021
Publication statusPublished - 5 Oct 2021


  • offshore wind farm
  • wave forecasting
  • observation data


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