Monitoring Arus dan Tegangan Pembangkit Listrik Tenaga Surya Menggunakan Internet Off Things
Bibliographic record
Abstract
Penerapan teknologi Pembangkit Listrik Tenaga Surya (PLTS) untuk memanfaatkan potensi energi surya yang tersedia merupakan solusi yang tepat dalam mengurangi ketergantungan terhadap penyedia energi PLN. Tetapi beberapa PLTS belum dilengkapi dengan alat monitoring arus dan tegangan.Termasuk pada PLTS di klinik LKC Dompet Duaffa Palembang tidak dilengkapi dengan alat monitoring arus dan tegangan. Dimana pada saat bekerja inverter maka baterai seharusnya terlepas dari inverter hal ini ditujukan untuk menghindari baterai terjadi over discharge karena daya keluaran pada inverter saat on grid akan bekerja maksimum. Oleh karena itu mengembangkan sistem monitoring arus dan tegangan secara Internet of Things (IoT) sehingga bisa dimonitor dari jarak yang jauh dan akan mengatur mekanisme charging dan discharging baterai pada konfigurasi PLTS, sehingga kontinuitas operasi inverter dapat tetap dijaga dalam mensuplai beban.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".