Understanding Cluster Wake-Induced Energy Losses off the U.S. East Coast
Bibliographic record
Abstract
Abstract. This study seeks to advance our understanding of energy losses caused by wind farm cluster wakes off the U.S. East Coast by utilizing advanced numerical models in conjunction with real-world, available data on existing and planned offshore wind sites. To this end, we have run simulations of existing and planned U.S. offshore wind lease areas using a typical-meteorological-year approach with a GPU-based Weather Research and Forecasting (WRF) model, where lease area layouts are generated based on most up-to-date project capacity information for each individual lease areas. To evaluate wake losses, we use an energy-loss-based definition of "wake shadow", as opposed to the traditional wind speed deficit assessment. A key insight from this study is that large wind speed deficits do not necessarily translate into significant energy losses. In addition, our results indicate that the conventional wind speed deficit method may underestimate the size of the wake area by up to 30 % compared to the proposed energy loss approach. These findings highlight the need to consider both wind speed deficits and energy losses when evaluating the wake effects of offshore wind farms and assessing future offshore wind development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".