Techno-Economic Analysis of Wind Power Generation in Mongo and Abeche, Chad
Bibliographic record
Abstract
The useful wind energy potential in the Sahelian part of Chad is estimated by considering two representative sites namely, Mongo and Abeche from a long series of in situ wind speed measurements over a period of thirty years at 10 m height from the ground.The Weibull distribution statistic was used and its parameters were used to show the influence of turbulence sources on the wind speed at 10 m.Vertical extrapolation of the Weibull parameters and wind speed at different heights from 10 m to 100 m provided more usable wind speeds.The results show that on average the wind is more intense and stable in Mongo than in Abeche and that the wind speed values increase with height with the influence of turbulence sources quantified at about 22.15% in Abeche and 19.93% in Mongo.The comparative study of the five turbines used shows that the De Wind D7 turbines with $0.057/kWh at 70 m in Abeche and Bonus 1MW/54 turbines with $0.067/kWh at 50 m in Mongo are better suited to produce energy according to their production capacity.The minimum and maximum electricity costs per kilowatt hour obtained using these two best turbines are very competitive compared to the cost of electricity in Chad (about $0.16/kWh) and that the installation of a wind farm in these cities could significantly improve the socio-economic situation of households.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".