Research on the influencing factors of wind power generation based on clustering and decision tree
Bibliographic record
Abstract
In this paper, the decision tree method is utilized to explore the influencing factors of wind power generation. This paper innovatively utilizes a combination of clustering and decision tree algorithms for data analysis. Firstly, the samples are categorized into three categories, high wind power generation, medium wind power generation and low wind power generation using K-means algorithm. Then, a decision tree model was applied to each category to obtain the proportion of feature importance. The results show that the key factors affecting wind power generation include motor torque, blade angle, electrical resistance and generator temperature. Compared to the traditional Adaboost algorithm, the new algorithm has a mean square error of no more than 3% and a coefficient of determination (R2) greater than 0.78. Compared to the Adaboost algorithm, the new algorithm has a 2.671% lower mean square error and an improved R2 of 0.135, which suggests that the new algorithm is more reliable in predicting wind power generation. Future research directions, this study can be extended by considering more factors that affect wind power generation, such as wind speed, wind direction, and air density.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".