Assessment of concentrated solar power (CSP) generation potential in Cameroon using a Multi-Criteria Analysis Method and Geographic Information System (GIS)
Bibliographic record
Abstract
This research aims to identify wet-cooled CSP (Concentrated Solar Power) solar power plants connected to the existing electricity grid in Cameroon. This study uses a hybrid approach which combines an MDCM-AHP method (Multi-Criteria Analysis Method - Hierarchical Analysis Process) and a GIS (Geographic Information System). The elements studied are the climate (Direct Normal Irradiance (DNI), temperature), orography (slope and elevation) and location (proximity to the electricity network, proximity to roads and railways, proximity to homes), in order to determine the weight of these different factors and combine them to obtain the final map. The method was used to map and visualize unsuitable areas, as well as to rank suitable sites. According to the results, over 42.35 % of the country's nland is unsuitable for the construction of CSP solar plants, mainly due to excessive land use. According to the results, 0,001 % of Cameroon's land is considered "Less suitable", 44 % "Suitable", 13.46 % "Highly suitable" and 0.01 % "Most suitable".The sensitivity study includes three different situations, namely the technical scenario (high consideration of technical factors such as DNI, temperature, elevation, and slope). The economic scenario (where economic factors such as proximity to electricity grids, roads, residential areas and watercourses are taken into account) and the equal-weight scenario (where all factors are equal) clearly demonstrate that we can find very suitable areas for the installation of CSP solar plants in Cameroon, particularly in the Far North region. A detailed analysis by region shows that the theoretical potential for CSP solar energy in regions with "Less Suitable", "Suitable", "Highly Suitable" and "Most Suitable" suitability is approximately 4242.25 TWh/year, 29328.658 TWh/year, 3965.25 TWh/year and 5.794 TWh/year. The overall potential amounts to approximately 37536.159 TWh/year for all ten Canadian regions. Thanks to the model, it has been possible to identify the most suitable regions for CSP investment and offers opportunities to carry out more in-depth studies in order to choose the right 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".