Wind Speed Clustering Using Linkage-Ward Method: A Case Study of Khaaf, Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of renewable energy for providing electricity is growing rapidly. Among others, wind power is one of the most appealing energy sources. The wind speed has direct impact on the generated wind power and this causes the necessity of wind speed forecasting. For better power system planning and operation, we need to forecast the available wind power. Wind power is volatile and intermittent over the year. For getting better insight and a tractable optimization problem for different decision making problems in presence of wind power generation, it is required to cluster the possible wind power generation scenarios. This article presents probabilistic wind speed clustering prototype for wind speed data of Khaaf, Iran. This region is known as one of the high potential wind sites in Iran and several wind farm projects is planned in this area. The average speed of wind for a ten-minute period measured at height of 40m over a year (2008) is used for clustering. From the result of this research, the most appropriate probabilistic model for the wind speed can be obtained.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it