A holistic framework to optimize embedding PV systems into building façades
Bibliographic record
Abstract
In addressing fossil fuel supply concerns and their environmental impacts, the building sector, as a major energy consumer, offers an opportunity for renewable energy integration. Among renewable energy sources , solar energy through photovoltaic (PV) panels on building façades stands out as a notable option, though fully realizing their potential remains a challenge. This study introduces a framework for the automated design of PV panels integrated into the façades of existing buildings, enabling thorough assessment based on energy efficiency, economic feasibility , and environmental impact. The process involves capturing the geometry of building envelopes, a deep learning model to identify façade surfaces for PV installation, and simulations to model PV generation and energy demand. An evolutionary multi-objective optimization algorithm is then employed to determine the PV system design parameters. The results of applying the framework to two university buildings in Alberta, Canada, are presented. For these cases, when equal weights are given to economic, environmental, and energy efficiency objectives, the optimal PV layout can achieve electricity self-sufficiency of 5.16 % and 6.78 %, with greenhouse gas emission rates of 18.26 and 15.69 g CO 2 -eq./kWh, respectively. The analysis illustrates that adjusting objective priorities yields different optimized solutions to balance competing factors. For example, prioritizing self-sufficiency increases the number of panels while focusing on financial return results in fewer panels and shorter payback periods . Although the financial feasibility of PV systems in Alberta's energy market is currently constrained by low electricity prices, the analysis highlights opportunities for improvement through government incentives and potential electricity price increases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".