Optimizing solar energy collection potential in high-rise residential buildings in urban areas
Bibliographic record
Abstract
High-rise residential buildings in urban areas face challenges in solar energy collection due to limited roof space, shading from nearby structures, and design constraints. Consequently, the interaction between passive solar gains through windows and active strategies such as photovoltaic (PV) systems plays a pivotal role in optimizing solar collection. This study develops a simulation-based optimization workflow that integrates EnergyPlus with the NSGA-II algorithm to minimize building net energy consumption and the life cycle cost of windows and PV systems on the building envelope, while ensuring thermal comfort as a constraint. Unlike previous studies, the approach considers PV and window design parameters while accounting for practical limitations such as standard PV module dimensions, realistic window sizes, and the variable optimal height for PV installation on each façade, which depends on shading from neighboring buildings.A case study in Toronto demonstrates that moderately priced PV panels with about 19% nominal efficiency, rooftop PV tilt angles around 32° to reduce snow losses, and higher U-value for high-performance windows offer the best balance of cost and performance. The optimization minimizes window widths to maximize PV coverage, while the optimal PV height varies by orientation, reflecting differences in solar access. Overall, the findings point to design strategies that balance cost, comfort, and energy consumption, offering a pathway for more efficient integration of PV in high-rise 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".