Economic and environmental evaluation of photovoltaic noise barriers
Bibliographic record
Abstract
Concerns over the growth in energy use and environmental impacts of energy production have increased the interest in the potential of photovoltaic electricity generation. This study assesses photovoltaic systems integrated into noise barriers along highways in Ontario. Firstly, a methodology to simulate solar radiation on tilted surfaces is reviewed. Measured hourly solar radiation data is used to simulate photovoltaic electricity generation at several orientations. The solar potential contribution to meet Guelph electricity demand at peak hours is determined. Hourly Ontario's wholesale market prices are combined with photovoltaic electricity generation for economic evaluation. Noise barriers costs are integrated for assessment of photovoltaic systems on noise barriers. Finally, energy payback time and CO2 emissions for photovoltaic systems are determined based on the emissions of the provincial mix of electricity generation. This research demonstrates an application of solar and economic models to evaluate performance and costs of photovoltaic systems. Maximum annual solar irradiation of 1502 kWh/m2/year at tilt 36° south oriented surface is calculated. At electricity peak demand time, the availability of solar resource is 57% of its peak level. The photovoltaic electricity cost ranges from C$0.30/kWh to C$1.25/kWh depending on photovoltaic on highway noise barrier configuration. CO2 emissions of photovoltaic on noise barriers vary from 0.041 tCO2/MWh to 0.058 tCO2/MWh.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".