Towards Zero-Energy Buildings: A Comparative Techno-Economic and Environmental Analysis of Rooftop PV and BIPV Systems
Bibliographic record
Abstract
The integration of photovoltaic (PV) systems in buildings is crucial for reducing reliance on conventional energy sources while promoting sustainability. This study evaluates and compares three energy generation systems: rooftop PV, building-integrated photovoltaics (BIPV), and a hybrid combination of both. The analysis covers energy production, economic feasibility through the levelized cost of electricity (LCOE), and environmental impact by assessing unreleased carbon dioxide (UCD). A residential building in Kerman, Iran, serves as the case study. The results indicate that rooftop PV exhibits the lowest LCOE at USD 0.023/kWh, while BIPV has a higher LCOE of USD 0.077/kWh due to installation complexities. The hybrid system, combining both technologies, achieves a balance with an LCOE of USD 0.05/kWh while maximizing energy generation at 16.2 MWh annually. Additionally, the hybrid system reduces CO2 emissions by 9.7 tons per year, surpassing the standalone rooftop PV (5.0 tons) and BIPV (4.7 tons). The findings highlight the synergistic benefits of integrating both PV systems, ensuring higher self-sufficiency and enhanced environmental impact. This research underscores the necessity of comprehensive urban energy planning to optimize renewable energy utilization and accelerate the transition toward zero-energy buildings.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".