Probabilistic access forecasting for improved offshore operations

Gilbert, C., Browell, J. and McMillan, D. (2021) Probabilistic access forecasting for improved offshore operations. International Journal of Forecasting, 37(1), pp. 134-150. (doi: 10.1016/j.ijforecast.2020.03.007)

Abstract

Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. This paper describes a novel method for producing probabilistic forecasts of safety-critical access conditions during crew transfers. Methods of generating density forecasts of significant wave height and peak wave period are developed and evaluated. It is found that boosted semi-parametric models outperform those estimated via maximum likelihood, as well as a non-parametric approach. Scenario forecasts of sea-state variables are generated and used as inputs to a data-driven vessel motion model, based on telemetry recorded during 700 crew transfers. This enables the production of probabilistic access forecasts of vessel motion during crew transfer up to 5 days ahead. The above methodology is implemented on a case study at a wind farm off the east coast of the UK.

• Item Type: Articles
• Additional Information: This work is supported by the EPSRC Supergen Wind Hub project ORACLES, EP/L014106/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Browell, Dr Jethro
• Authors: Gilbert, C., Browell, J., and McMillan, D.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: International Journal of Forecasting
• Publisher: Elsevier
• ISSN: 0169-2070
• ISSN (Online): 1872-8200
• Published Online: 12 May 2020
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in International Journal of Forecasting 37(1): 134-150
• Publisher Policy: Reproduced under a Creative Commons License