Optimization of Solar Energy System for the Electric Vehicle at University Campus in Dhaka, Bangladesh
Bibliographic record
Abstract
The incorporation of renewable energy and the transportation system can be significantly beneficial for the economy and environment of Bangladesh. The main energy source for vehicles in Bangladesh are the country’s natural gas and fuel. However, due to the rapid depletion of the gas reserve, soaring gas prices and global warming, alongside the environmental pollution caused by burning fuel, this raises concerns about these energy sources. Renewable energy offers a plausible solution to these problems. This paper’s objective is to focus on the maximum usages of a solar photovoltaic (PV) system in electrical vehicles and to minimize the environmental impact in terms of CO2 emission. This system may be partially used to power up the electric vehicle with a charging facility and contribute excess power to the national grid. The modeling, with its optimal analysis of the green transportation system, is simulated using the Hybrid Optimization of Multiple Energy Renewables (HOMER) software. The energy produced by the PV system can provide up to 13,792 kWh/year. Approximately 21% of the total production can be used in the charging station for charging the electrical vehicles and the rest of the energy can contribute to the national grid. Moreover, using the proposed concept of green transport will ultimately reduce greenhouse gas emissions by 52,944 kg/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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".