AN IMPROVED SPECTRAL CLUSTERING ALGORITHM FOR LARGE-SCALE WIND FARM POWER PREDICTION
Bibliographic record
Abstract
Aiming at reaching the balance between calculation efficiency and power prediction accuracy of wind farms, two improved spectral clustering (SC) algorithms and their application framework are proposed.For classical k-way Ng-Jordan-Weiss SC, the clustering sample space is composed of k eigenvectors, which may lose part of structural information and may not reach accurate clustering results.To improve the accuracy and stability, we proposed to cluster with feature expansion and the Cuckoo Search (CS) algorithm.We extended the clustering eigenspace from k eigenvectors to 2k to improve the clustering accuracy.To avoid following into local optimum while extending the eigenspace, the CS algorithm was introduced to search for better initial points instead of the random choice method.To apply the proposed algorithm for wind power prediction, wind turbines with similar wind regime were designated to the same group using the proposed SC algorithm.The power prediction model was established for each wind turbine group, and the output power of the entire wind farm was obtained by superposition.Experimental results indicated that the clustering accuracy is improved and the results of multiple clustering hold steady, which meets the requirement of accurate and timely prediction of wind farm power.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".