Computer Tool for Sizing and Economic Assessment of Grid Connected Photovoltaic System
Bibliographic record
Abstract
Thanks to the location close to the Equator line, Colombia offers an advantage location for solar applications. This is the case of Neiva, a city located in the south of Colombia where the average temperature and irradiance are 31 °C and 4 kW/m2 respectively. This paper describes the development of an investment payback solar calculator (PSC) software for residential and commercial PV solar applications in Neiva. The PSC uses both the average solar energy and the electricity prices to estimate the rate of return on investment, the payback period (PBP), the net present value (NPV) as well as the environmental impact. The PCS software is then evaluated using a historical and statistical information from NASA, adjusted with the results of a small scale 1 kW grid connected PV system production. The results show that the PCS software can satisfactorily indicate the PV system size and perform an economic evaluation of the system to be implemented. The results obtained using the application developed were compared to two commercially available software Sunny Design and HOMER. The NPV and the PBP values showed that the proposed application results are like those delivered by the commercial software. Furthermore, the economic evaluation given by the developed tool uses additional information which makes it more accurate in countries where socioeconomic stratum is presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".