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Probabilistic access forecasting for improved offshore operations
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DOI：10.1016/j.ijforecast.2020.03.007.png)
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摘要
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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 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. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters.
Keyword:
Wind energy
Offshore access
Probabilistic forecasting
Multivariate forecasting
Forecast visualisation
Generalised additive models
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