AN ASSESSMENT OF NIGERIA WIND ENERGY POTENTIAL BASED ON TECHNICAL AND FINANCIAL ANALYSES
Bibliographic record
Abstract
The energy requirement of Nigeria is increasing exponentially with little projects available to carter for this increasing demand. The primary source of energy in the country which is fossil fuel creates environmental pollution and is also finite in nature. Hence, there is a serious need to look for other alternative ways to meet up with the energy requirement of the country. This paper analysed some of the economic and sustainability benefits for Nigeria by deploying and integrating wind energy into her energy mix. The study was done with the RETScreen Clean Energy software tool, designed by Natural Resources Canada. The study began with a brief review of the various wind energy resource assessment done previously in the country and Maiduguri town was selected as the area of study from the reviews. The technical and financial analyses of the study showed that if the project is implemented it will be beneficial to Maiduguri town and Nigeria in the long run. The process of electricity generation from 100 units of VESTAS V80 in Maiduguri yielded MWh 525,600 and saves about 202,881.6 tonnes of CO2. Without incentives, the financial analysis showed that the project is not financially viable with the equity payback greater than the project life. Key issues affecting the development of wind energy technology in Nigeria were also discussed.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".