ANALISIS SISTEM KELISTRIKAN PADA PEMBANGKIT LISTRIK TENAGA SURYA ON-GRID KAPASITAS 25 KWP DI BADAN PERENCANAAN PEMBANGUNAN DAERAH (BAPPEDA) PROVINSI BALI
Bibliographic record
Abstract
In the 2020 fiscal year, the Ministry of Energy and Mineral Resources of the Republic ofIndonesia provided PLTS On-Grid grants to the Province of Bali as many as 10 PLTS locationsin Denpasar City. One of them is in the Regional Development Planning Agency (BAPPEDA) ofBali Province with an installed capacity of 25 kWp which is connected to the PLN network.BAPPEDA Bali is an example of the NRE Development Program and the Regional Medium-Term Development (RPJMD) that supports the PV mini-grid sector. This research wasconducted to determine the performance of the PLTS electrical system and to simulate theresults of PLTS production using Helioscope software so that it can compare the simulationresults of 2 scenarios with real conditions to determine the level of effectiveness in savingelectricity bills and the factors that influence the results of PLTS energy production. The resultsshowed that the potential for electrical energy generated for a year from the simulation ofScenario 1 and Scenario 2 was 38.90MWh and 39.07MWh. It is known that the real energyproduction from July to December 2021 is 18,083 kWh with the simulation results of scenario 1and scenario 2 from July to December 2021 which are 19,810 kWh and 20,015 kWh. Thedifference between real energy production and the simulation results in scenario 1 and scenario2 is 1,727kWh with a percentage of 8.72%, and 1,931kWh with a percentage of 9.65%. Thepercentage of savings obtained for 6 months in 2021 compared to 6 months in 2020 is 56.42%with a saving value of Rp. 18,783,953.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".