Techno-Economy Study of wind energy in Khvaf in Razavi Khorasan Province in Iran
Bibliographic record
Abstract
In this paper, the 10-minutes period measured wind speed data at different heights (10 m, 30 m, and 40 m) are presented for Khvaf, which is one of the major counties with high wind potential in Khorasan provinces in Iran. To the author’s knowledge, there hasn’t been any assessment works in the mentioned site. From the primary evaluation and by determining mean wind speed and also the Weibull function, the results show that the measurement site falls under class 7 of the International System Wind Classification, which means that the station has very suitable conditions for installing and operating wind farms. On the other hand, a new approach is utilized for evaluating potential power of a region based on comparison between maximum power generation pattern and daily and monthly energy consumption patterns. Furthermore, by using wind roses of speed and turbulence simultaneously, the best direction for installing wind turbines is determined. On the other hand, the situation of topography and surface conditions of Khvaf station has been analyzed; because of its smooth surface, it is appropriate for installing wind turbines. Besides, several types of Vestas Company turbines have been compared by their capacity factors and three of them with highest capacity factor are selected for economic evaluations. One of the important issues in the assessment of wind energy potential is the economic evaluation. This is a major gap that many researches do not have sufficient attention to it. Hence, an economic analysis was performed based on NPV, IRR, and Normal Payback methods in order to select the best wind turbine for this site.
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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) | 0.000 | 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 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".