PVSYST-Based Solar Power Plant Planning
Bibliographic record
Abstract
This research uses the PVSyst software which can plan an off-grid SPP system and find out how much electricity is generated in the Auditorium Building Campus 2, Ujung Pandang State Polytechnic. PVSyst is a software package that is used for the learning process, sizing, and data analysis of the PV mini-grid system. PVSyst is divided into grid connected systems, standalone systems, pumping systems. PVSyst is also equipped with a database from a wide and diverse range of meteorological data sources, as well as data on PV mini-grid components. In planning a PVSyst-based solar power plant, it can be used to find out how much electrical energy is generated to meet the electricity needs of the Campus 2 Auditorium Building, Ujung Pandang State Polytechnic. In this study using the PVSyst software according to the proposed load requires an average energy of 482 kWh/day, therefore, the panel module used is the polycrystalline type model CS3W-410P-HE manufacturer Canadian Solar Inc, where each panel unit used has a nominal power of 410 WP requires 422 modules, 2 modules in series and 211 modules in parallel. The battery used is a lead acid type with a 12-CS-11PS model manufactured from Rolls. The batteries used are 195 batteries, of which 3 batteries are installed in series and 64 batteries are installed in parallel. The controllers needed in this design are 50 units with the FLEXmax 80-36V model manufactured from Outback. The technology of this controller is an MPPT converter. In planning Solar Power Plant using PVSyst in the Auditorium Building Campus 2 of Ujung Pandang State Polytechnic, it produces electricity of 227,122 kWh per year.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".