Planning for offshore wind: An integrated smart approach combining NREL classification and TOPSIS
Bibliographic record
Abstract
• Integrated and High-Resolution Methodology A structured evaluation framework combining NREL classification and TOPSIS multi-criteria analysis integrates high-resolution ERA5 datasets and advanced statistical modeling for precise offshore wind resource assessment. • Validation through Operational Data Comparison Estimated energy production aligns closely with operational values in France and Denmark, confirming the reliability of the approach. Deviations observed in China, England, and the USA highlight opportunities for optimization through technological advancements. • Identification of Underutilized and High-Potential Sites Significant underutilization is detected in locations such as China and England, where theoretical potential vastly exceeds current output. Unexploited sites in Argentina and Canada demonstrate strong feasibility for offshore wind deployment. • Global Applicability and Adaptability The developed methodology is adaptable to diverse geographic contexts and provides a reproducible decision-support framework for offshore wind site prioritization worldwide. • Pathways for Future Enhancements Incorporation of metaheuristic optimization algorithms, alternative multi-criteria methods, and artificial intelligence for performance modeling is proposed to refine site selection. The integration of environmental and socio-economic indicators will further enhance strategic offshore wind energy planning. Development of offshore wind farms requires consideration of complex parameters, including maritime conditions, coastal distance, water depth, and seabed stability, with significant differences compared to onshore configurations. The current assessment evaluates offshore wind energy potential through integration of critical technical and economic factors. Evaluation covers 25 selected locations, including five operational sites: P1(China), P6 (Denmark), P8 (USA_01), P11 (England), and P19 (France). The proposed analytical framework combines statistical modelling with multi-criteria decision analysis for comprehensive site evaluation. Wind potential is classified according to NREL standards, and sites with insufficient energy output are excluded. Wind modelling is based on the Weibull distribution. Among the nine methods evaluated for estimating the k and c parameters, the Maximum Likelihood Method, the Least Squares Method, and the WAsP Method provided the most accurate performance. Derived parameters are incorporated into a TOPSIS-based multi-criteria analysis using indicators such as wind speed, power density, capacity factor, water depth, and proximity to shore. Evaluation results confirm strong alignment between predictions and operational values for sites such as P6 and P19. Substantial positive deviations are observed for sites P1 and P11, reflecting underutilized wind resources with potential for optimization through advanced turbine technology. Production costs range between 0.008 and 0.028 $/kWh, revealing economic disparities among sites. Site P4 (Canada) demonstrates consistent top-tier performance across five sensitivity scenarios with varying weightings. Integration of NREL classification with TOPSIS proves effective in offshore wind prioritisation. The system enhances sustainable energy planning through high-precision assessment and balanced evaluation of technical and economic indicators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".