Status: this preprint is currently under review for the journal WES.
Economic and design optimisation of a 15MW floating offshore wind platform using time-series forecasting
Craig White,Victor Benifla,and José Cândido
Abstract. This work proposes a structural and economic optimisation framework applicable to floating semi-submersible platforms, demonstrated here for a 15-MW offshore wind design. A genetic algorithm was developed that can seek a multi-objective solution to minimise mass whilst respecting the constraints of loads acting upon the system. Statistical and machine learning methods are then employed to forecast near and far term costs of the platform under a range of scenarios. Finally, Levelized Cost of Energy is calculated to gauge the technical and economic viability. Results show that a mass reduction of 9 % is possible. With optimal costs predicted under the SARIMAX scenario of 4404.56 €/t, 24.1 % under the average. The optimised platform results in an LCoE reduction of 2.08 %.
Received: 01 Oct 2025 – Discussion started: 15 Oct 2025
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This paper looks to create sensible mass reductions to the new commercial-scale floating offshore wind platforms which must reduce in cost if they are to be deployed at scale. To do this, a response-amplitude numerical model tested the wind turbine whilst a genetic algorithm optimised the geometrical design. Then a time-series forecasting tool the blends machine learning and statistical models to predict future prices to accurately cost the platform.
This paper looks to create sensible mass reductions to the new commercial-scale floating...
