A Study on the Power Production Potential of Parking Space Solar Shelters in Kingston, Ontario
Bibliographic record
Abstract
The implementation of solar shelters over top of parking spaces has the potential to make the production of renewable energy a secondary function of parking lots without impeding their ability to function as parking locations. This has the capacity to reduce the amount of natural space converted to solar farms as solar energy becomes more common. In addition, if these shelters are outfitted as charging stations for electric vehicles, they could serve as a driver for a cultural shift towards a more sustainable vehicle fleet. Implementation of this technology has begun on a small scale in San Diego, California and this project assessed the feasibility of implementation in Kingston, Ontario. This study set out to determine how much energy could be produced by a solar shelter over one parking space and how many parking spaces would be required to produce 1% of Kingston’s total electricity consumption. An insolation model was written in C, which used past climate data and mathematical models to incorporate the effects of latitude, cloud cover and snow. This model was compared to the current production in San Diego to check for validity. Since the insolation model was deemed to be valid, the results were used in conjunction with typical solar panel efficiencies in Kingston to calculate the potential energy production per structure. This was then used to determine the number of structures that would be required to provide 1% of Kingston’s electricity. Through literature review, it was determined that although snow on the panels would have a drastic effect on power production, it would not remain on the panels long enough to cause a significant effect. It was found that a single parking space in Kingston would be capable of generating 5500±_800^1000 kWh/year using the single-axis tracking model that is currently being implemented in San Diego, although a dual-axis tracking model would be capable of generating 11% more energy. Using the current prototype, Kingston would require implementation across about 2750 parking spaces in order to provide 1% of its electricity and it has ample locations which would be suitable. However, due to the current $40,000 price tag per structure, the current buy-back period is about 55 years which makes the current technology not economically feasible without lowering the cost or increasing the efficiency.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".