Economic and design optimization of a 15 MW floating offshore wind platform using time series forecasting

A structural and economic optimization framework applicable to floating semi-submersible platforms, demonstrated here for a 15 MW offshore wind design, is presented. A genetic algorithm was developed that can seek a multi-objective solution to minimize mass whilst respecting the constraints of loads acting upon the system. Statistical and machine learning methods are then employed to forecast short- and long-term costs of the platform under a range of exogenous data scenarios, selected to support and boost forecasting accuracy alongside a hybrid forecasting method. Steel mass was reduced from 3916 to 3273 t whilst respecting platform response constraints. The uncertainty in steel prices has the most significant impact on CAPEX, approximately EUR 150–200 million at the 1 GW level. Levelized cost of energy (LCoE) is calculated to gauge the technical and economic viability, with EUR 3–5 MW h −1 variation across forecasts.