Investigating the optimization potential of daylight, energy and occupant satisfaction performance in classrooms using innovative photovoltaic integrated light shelf systems
Bibliographic record
Abstract
Building-integrated photovoltaics (BIPV) show potential as a means of efficient solar energy harvesting. This paper introduces a multi-objective framework for finding the optimized design of parametrically modeled photovoltaics integrated light shelf systems to maximize their benefits to the indoor environment and users. Since the geometric design of traditional shading devices is restricted to rectangular-shaped panels, we aim to attain a clearer perception of the potential advantages of employing panels with novel design alternatives. First, we developed parametric models of internal and external light shelves; each consisted of either a surface with a curved section or a 4-point planar surface. Next, the assessment of daylight and energy operation along with occupants' thermal and visual comfort was carried out using the environmental plugins of Honeybee and Ladybug. Furthermore, to decrease the required lighting energy and upgrade users' visual satisfaction by providing favorable illuminance levels required for a specific task, we divided the classroom into alterable lighting zones. Lastly, the optimization process was performed via the Octopus plugin for Grasshopper, and optimal solutions were identified. Based on the numerical results of the yearly simulations, we gained noticeable energy-saving up to 29% while improving occupants' thermal and visual comfort.
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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 | 0.000 | 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".