Offshore wind power assessment on the western coast of Thailand
Bibliographic record
Abstract
This paper presents an offshore wind resource assessment and offshore wind power feasibility analysis on the western coast of Thailand using coupled mesoscale atmospheric and microscale Computational Fluid Dynamics wind flow modeling, along with a climatic database. Offshore wind maps at elevations of 80 m, 90 m and 120 m above mean sea level (amsl) are presented and validated with measured wind datasets gathered from nine met masts installed along the coast of the Andaman Sea and the Strait of Malacca on the western part of Thailand. Results show that, at an elevation of 120 m amsl, the northern region near Myanmar is characterized by wind speeds in the range of 5.5 to 6.0 m/s, while the area around Phuket Island has wind speeds in the range of 4.6 to 5.3 m/s. In line with the Thai energy policies for wind power plant development, the annual energy production, the capacity factors, the levelized cost of energy (LCOE), the CO2eq emission avoidance, and the required Feed-in-Tariff (FiT) are analyzed for wind power plants integrating 3.3 MW, 8.0 MW and 9.5 MW offshore wind turbine generators. Results show that offshore wind power plants with 3.3 MW wind turbine generators could produce over 13 GWh of electricity per year under the Very Small Power Producer (VSPP) scenario for 10 MW wind power plants. For its part, 50 MW wind power plants under the Small Power Producer (SPP) scenario could produce 68 GWh/yr, and nearly 123 GWh/yr for SPP wind power plants with 90 MW of installed capacities. With capacity factors in the order of 16%, the lowest LCOE is 0.188 USD/kWh and the CO2eq emission avoidances reach 774 tonnes CO2eq per year. Finally, the required FiT to sustain the development of VSPP offshore wind power plants in the vicinity of Phuket Island is in the range of 0.314 to 0.688 USD/kWh.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".