the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Oct 2025
Economic and design optimisation of a 15MW floating offshore wind platform using time-series forecasting
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 %.
- Preprint
(1356 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 20 Dec 2025)
-
RC1: 'Comment on wes-2025-197', Anonymous Referee #1, 20 Nov 2025
reply
-
AC1: 'Reply on RC1', Craig White, 06 Dec 2025
reply
Dear reviewer, thank you for your comment. I agree with the assessment on the benefit of a more integrated approach to the technical and economic optimization. Both models were developed separately but a combined future analysis could include economic constraints within the design optimization framework. This could yield additional benefits especially if design constraints regarding motion are tightened, which could limit the potential for mass reductions and a multi-objective framework could improve results. An integrated approach could allow for certain LCoE targets to be set which govern economic constraints and provide a geospatial context alongside auctions and could form an interesting development. Also, a more system-integrated approach could consider the platform dimensions and optimized reductions within the context of the mooring system, RNA and tower etc, alongside benefits for installation, port requirements and O&M processes.
In regards to the novel nature of the work, the optimization study here is novel in its application to the Volturn-US platform using the developed design optimization framework, and applying its results to a detailed cost analysis and its integration within the RAFT framework, which solves the amplitude response of the floating turbine, under the design conditions and constraints at each iterative step of the design optimization. The economic method takes proven methods (SARIMAX long term, XGBoost short term) and applies them to structural steel which for marine renewables presents a novel approach in this area, alongside a hybrid forecast which selects the best performing model at each assessment interval. The time-series forecasting methods incorporated into the models were applied specifically to improve the predictions for materials with a direct effect on floating wind LCoE, which to the author’s knowledge has not been done previously in literature in this context, and is of high relevance and importance considering current CAPEX and project financing concerns.
I agree with the limitations of the literature review. Time series forecasting has limited previous work within the context of marine renewables and marine grade structural steel, which is a driving factor for this work. Further citations could be included on structural steel forecasting, outside the marine context which are available and can be included in a future iteration of this paper, and would add more context to the study and could serve as a comparison to the accuracy and results of the timeseries forecasts.
We defined the method within Figure 2, but this was high-level and does not capture all information flows and processes which may help the reader understand the method, including the internal processes in the forecasting tools. This would also raise the level of detail to match Figure 2a. Equation (6) term were mistakenly omitted which is an easily fixed oversight. Section 3.3.2 could be developed further. Especially with LCoE calculations and the integration of generic CAPEX items without direct influence on this study’s focus. There are more figures which could better explain the economic method but were omitted due to the WESC page limit, but can be rectified through text revision, and a more detailed process flow chart and explanation, and perhaps more explanatory process figures if space allows.
On Figure 5, (L) and (R) denote the relevant axis but this could be improved and I do agree it is hard to read the season-naive and zero-mean forecasts for each. Careful selection here could also provide other clear figures which better show the model selection and performance, and will be considered for a revised edition, such as limiting the charts to RMSE or MASE, and including possibly timeseries forecast evolutions with different input criteria, model types or train-test splits.
We thank the reviewer for these insightful comments about our submission. A future revision can take on board this feedback and build a stronger literature section to understand how to make this novel contribution to floating offshore wind, which is through this optimization and future price forecasting tool. We believe time-series forecasting within the framework of floating offshore wind design is a new and exciting challenge, and a revised edition will aim to make this objective known with greater clarity. Kind regards.
Citation: https://doi.org/10.5194/wes-2025-197-AC1
-
AC1: 'Reply on RC1', Craig White, 06 Dec 2025
reply
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 203 | 36 | 17 | 256 | 17 | 22 |
- HTML: 203
- PDF: 36
- XML: 17
- Total: 256
- BibTeX: 17
- EndNote: 22
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This paper presents two independent analyses: minimization of the mass of a 15-MW floating platform and HRC steel price forecasting to 2033. Although the analyses are both connected to floating offshore wind LCOE, they are conducted separately and applied sequentially, which does not appear to result in any novel insights into potential systems interactions for economic and design optimization. Each analysis appears to be based on previously established methods. The abstract lists the best values from each analysis but does not present a combined result.
The literature review includes several relevant citations for floating offshore wind platform design and cost assessment, however it lacks any references to previous academic work on modeling steel or commodity prices. This section could also be improved by explaining how each cited work is relevant to the current research rather than simply summarizing its contents.
The methods section provides a clear description of the design optimization methodology. The price forecasting is less clear. Specifically, input data are discussed only generally and symbols in Eq. 6 are not defined.
Table 2 presents the results of the design optimization in a complete and concise format. Figure 4 is also a useful representation of the price forecasting results. Figure 5 appears to show poor performance of the forecast models (very high RMSE, MASE, and sMAPE) and it is difficult to identify which axis (R or L) corresponds to which gray lines in the upper left subfigure.
If the authors choose to revise this manuscript, a new version should clearly identify how the work represents a novel contribution within the context of prior research.