Probabilistic access forecasting for improved offshore operations
Gilbert, Ciaran and Browell, Jethro and McMillan, David (2021) Probabilistic access forecasting for improved offshore operations. International Journal of Forecasting, 37 (1). pp. 134-150. ISSN 0169-2070 (https://doi.org/10.1016/j.ijforecast.2020.03.007)
Preview |
Text.
Filename: Gilbert_etal_IJF_2020_Probabilistic_access_forecasting_for_improved_offshore_operations.pdf
Final Published Version License: Download (1MB)| Preview |
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 safetycritical 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 datadriven 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.
ORCID iDs
Gilbert, Ciaran ORCID: https://orcid.org/0000-0001-6114-7880, Browell, Jethro ORCID: https://orcid.org/0000-0002-5960-666X and McMillan, David ORCID: https://orcid.org/0000-0003-3030-4702;-
-
Item type: Article ID code: 72110 Dates: DateEvent1 January 2021Published12 May 2020Published Online21 March 2020Accepted2 August 2019SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > EnergyDepositing user: Pure Administrator Date deposited: 21 Apr 2020 09:31 Last modified: 16 Nov 2024 01:15 URI: https://strathprints.strath.ac.uk/id/eprint/72110