ESTIMATION OF THE ECONOMIC EFFICIENCY OF AN INVESTMENT PROJECT FOR THE INTRODUCTION OF SOLAR PANELS IN BUILDINGS OF AN ADMINISTRATIVE QUARTER IN THE MUNICIPALITY OF PLATEAU (CÔTE D’IVOIRE)
Bibliographic record
Abstract
The use of renewable energy sources is a topical direction in the development of the energy industry in the modern world. However, when investing, choosing the optimal strategy is essential to keep costs down. The goal is to evaluate the efficiency of using solar panels parallel to the electric grid for the plateau municipality, the Republic of Cote d’Ivoire. The objectives of the study in this regard are to determine the strategy to the greatest extent, affecting the reduction of the investment cost when introducing a solar battery project, the calculation of economic indicators that allow us to assess the risks when making decisions to implement the investment. For the assessment, a technique is presented that differs from the existing complex taking into account specific indicators (average annual insolation, efficiency of solar panels, temperature coefficient, price of 1 M ^ 2 solar panels, transportation costs, cost of installation work, depreciation period, maintenance costs, property tax, the cost of training employees. The methodology includes the author’s recommendations, taking into account the specific features of determining the indicators of the annual economic effect, NPV. IRR, DPP for a solar battery project. A comparative analysis of an autonomous SES and operating in parallel with the electric grid. Conclusions: SES in parallel with a network of solar power plants. reduces the cost of the project investment, and also hinders the payback period of the project.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".