Planning for offshore wind: An integrated smart approach combining NREL classification and TOPSIS
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Notice bibliographique
Résumé
• 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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle