Applications of Traditional and Concentrated Photovoltaic Technologies for Reducing Electricity Costs at Ontario Data Centers
Bibliographic record
Abstract
Demand for cloud-based applications and remote digital storage is increasing. As such, data center capacities will need to expand to support this shift in computing. Data centers consume substantial amounts of electricity in support of their operations, and larger data centers will mean that more energy is consumed. To reduce electricity bills, data center operators must explore innovative options, and this thesis proposes leveraging solar technology for this purpose. Three different photovoltaic and concentrated photovoltaic costing scenarios, as well as four different Ontario-based electricity tariff scenarios – time-of-use, feed-in tariff, power purchase agreement, and a peak-dependent electricity charge involving the province’s global adjustment fee – will be used to determine if there is a business case for using solar technology at data centers in Ontario to reduce energy costs. Discounted net present value, return on investment, internal rate of return, and levelized cost of electricity will be calculated to determine the economic viability of solar for this application, and both deterministic and stochastic results will be provided. Sensitivity of the four metrics to variability from energy yield, operations and maintenance costs, as well as system prices will also be presented.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".