Technological Paradigm Between the Production of Photovoltaic and Thermal Solar Energy in Sahelo-Sahelian Countries
Bibliographic record
Abstract
The energy sector in Sahelian countries such as Burkina Faso, Mali and Niger is facing enormous challenges. Indeed, electricity shortages, characterized by recurrent breakdowns of means of production and import dependency due to an unfavorable energy mix, are numerous in these countries. Concerns about the high costs of facilities and operations, as well as environmental pollution, not forgetting to mention the matter of the countries' energy sovereignty, have led to the desire to use reliable, profitable and adapted means of production. The aim of this study is to review the literature on electric power generation systems in the world, in Africa and in Sahelian countries, with an emphasis focus on photovoltaic and thermal solar energy. In addition, it highlights, in views of the problems raised, works that have suggested solutions. To achieve this, scientific articles, theses and other scientific documents dealing with electric power generation systems from 2014 to 2024 have been consulted. This enabled us to get knowledge on the various electric power generation systems, to know their advantages and limitations, and above all the perspectives in terms of electric power generation. Additionally, they provided a source of inspiration for setting up an electric power generation system in line with the realities of the countries of the Alliance of Sahel States (ASS).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".