Development of Wind Turbine Generator and Solar Hybrid Power System Model for Rural Electrification
Bibliographic record
Abstract
The countries that are most energy-consuming, where there are industrial developments, where the energy demand is highest are the advanced and developing countries in the world (Mustafa, 2018). For instance, the average power per capital (watts per person) in the United States is 1,377 Watts. In Canada, it is as high as 1,704 Watts per person and in South Africa; it is 445 Watts per person. The average power per capital in Australia is 1,112 Watts and in New Zealand it is 1,020 W per person. Whereas, the average power per capital (watts per person) in Nigeria is 14 W per person. (Austin, O. O et.al, 2020). Also, power supply in many parts of Africa is erratic and characterized with a lot of faults and outages. In Nigeria, it is estimated that only 40 % of Nigerians are connected to the national grid and the connected population are exposed to frequent power outages (Abubakar et al, 2015, Austin O.A, 2020). Unfortunately, the effects of incessant power supply have destroyed many industrial activities, reduced employment and has increased crime activities in many parts of the continent (Africa). Therefore, in order to provide urgent solution to these problems and satisfy the high energy demand in African residential and industrial environments, electrical energy should be reliable, affordable, effective, and sustainable. This calls for an urgent establishment of alternative Renewable Hybrid Power Supply System which will provide continuous, reliable and effective power supply to the consumers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".