On the selection of wind turbine generator based on ARMA time series
Bibliographic record
Abstract
By increasing the global warming and environmental challenges the industrial countries commit to reduce the gas emissions and develop the renewable energy based on the Kyoto protocol and also renewable portfolio standard (RPS). Among them, wind resources are more important because of the progress in the technology of its facilities to convert wind to electricity. Wind turbines manufacturer companies improve the efficiency of their facilities during the last decade. On the other hand, the wind generator firms and investors intend to select a suitable wind turbine generators (WTGs) based on the wind distribution in a specific region. In this study, Output power (MW) of four different types of WTGs has been compared. The wind speed data has been collected for 10 years from 2000 to 2009 from Canada Swift Current region. Then, the model of wind speed has been predicted with ARMA time series. In the next stage, the output power of each WTG has been determined with probability method. Finally, the suitable WTG is recommended according to the output power produced.
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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) | 0.001 | 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 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".