Design and Evaluation of On-Grid Solar Rooftop Power Plant for Tower I of Ministry 3 Building in the New Indonesian Capital City
Bibliographic record
Abstract
The increasing need for electrical energy has encouraged the Government of Indonesia to develop renewable energy, including rooftop solar power plants (SPP) as part of the strategy to achieve a $23 \%$ renewable energy mix by 2025. This study aims to design and evaluate an on-grid rooftop PV system for Tower 1 of Kemenko Building 3 in the New Capital City of Indonesia. This system is expected to support the green building concept and contribute to the Net Zero Emission target. Using PVsyst 7.2 software and weather data from Meteonorm 8.0 (Pvsyst database), simulations were conducted to determine the optimal configuration for solar energy production. The research location is at coordinates $0.9638^{\circ} \mathrm{S}$, and $116.6985^{\circ} \mathrm{E}$ with a roof area of $875 \mathrm{~m}^{2}$. After being simulated using PVsyst, the optimal configuration for annual energy production was obtained at 67.3 MWh with a solar panel tilt angle of 10° and an azimuth of 0° (facing north). The SPP system is designed using 550 WP monocrystalline modules arranged in 5 strings with 16 modules in each string, as well as an inverter from Canadian Solar with a capacity of 40 kW. Technical analysis also recorded a performance ratio (PR) of 0.835, indicating an operational efficiency of around $83.5 \%$ of ideal conditions. The proposed SPP configuration is capable of producing optimal energy and supporting the development of renewable energy in new urban areas. Implementing of this rooftop SPP is also an important step in helping the sustainable development plan in the New Capital City of Indonesia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".