Analyzing Sustainable Energy Opportunities for a Small Scale Off-Grid Facility: A Case Study at Experimental Lakes Area (ELA), Ontario
Bibliographic record
Abstract
This thesis explored the opportunities to reduce energy demand and renewable energy feasibility at an off-grid science “community” called the Experimental Lakes Area (ELA) in Ontario. Being off-grid, ELA is completely dependent on diesel and propane fuel supply for all its electrical and heating needs, which makes ELA vulnerable to fluctuating fuel prices. As a result ELA emits a large amount of greenhouse gases (GHG) for its size. Energy efficiency and renewable energy technologies can reduce energy consumption and consequently energy cost, as well as GHG. Energy efficiency was very important to ELA due to the elevated fuel costs at this remote location. Minor upgrades to lighting, equipment and building envelope were able to reduce energy costs and reduce load. Efficient energy saving measures were recommended that save on operating and maintenance costs, namely, changing to LED lights, replacing old equipment like refrigerators and downsizing of ice makers. This resulted in a 4.8% load reduction and subsequently reduced the initial capital cost for biomass by $27,000, by $49,500 for wind power and by $136,500 for solar power. Many alternative energies show promise as potential energy sources to reduce the diesel and propane consumption at ELA including wind energy, solar heating and bio-mass. A biomass based CHP system using the existing diesel generators as back-up has the shortest pay back period of the technologies modeled. The biomass based CHP system has a pay back period of 4.1 years at $0.80 per liter of diesel, as diesel price approaches $ 2.00 per liter the pay back period reduces to 0.9 years, 50% the generation cost compared to present generation costs. Biomass has been successfully tried and tested in many off-grid communities particularly in a small-scale off-grid setting in North America and internationally. Also, the site specific solar and wind data show that ELA has potential to harvest renewable resources and produce heat and power at competitive rates compared to diesel and propane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".