Application of semitransparent photovoltaics in transportation infrastructure for energy savings and solar electricity production: Toward novel net‐zero energy tunnel design
Bibliographic record
Abstract
Abstract Lighting subsystems account for up to 50% of the energy consumption of a typical tunnel. Day‐time lighting levels account for over two‐thirds of the total system lighting power; their periodic nature creates daily peaks in the tunnel's energy load profile. This paper studies the integration of semitransparent photovoltaic (STPV) cells into sunscreen structures installed above tunnel entrances to reduce tunnel lighting requirements and offset their day‐time lighting loads using energy generated from PVs. The electrical lighting load of a typical 1‐km length road tunnel with and without STPV sunscreen structures was modeled to establish the potential for energy savings. Using a daylighting and energy modeling plug‐in called DIVA, the transparencies and ratios of photovoltaics (PV) to glass of a STPV sunscreen that are in accordance with the luminance reduction code requirements were determined. Reduced lighting requirements over the whole tunnel length, including the threshold, transition, and interior lighting zones of the tunnel were considered, resulting in significant energy savings. The annual power production of the sections covered with STPV was then simulated using the PVsyst software. The integration of PV cells resulted in an annual energy production that reduced annual net‐energy use by up to 7% and with the potential to reduce electric lighting loads by up to 60% during the day‐time. Results also demonstrated that STPV sunscreens have the potential to meet luminance requirements if supplemented with an intelligent lighting control system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".