Opportunities for renewable energy in large Saskatchewan irrigation projects evaluated in HOMER pro software
Bibliographic record
Abstract
With a less predictable climate, irrigated agriculture may contribute to global food security. Irrigation requires large water and energy inputs – creating a water-food-energy nexus. In Saskatchewan, irrigation typically sources energy from the provincial electrical utility, SaskPower. Using current utility infrastructure, dependence deepens the use of conventional fossil fuel power. With major cost decreases, renewable energy alternatives are increasingly techno-economically competitive. In undertaking energy system modelling using HOMER Pro software, this study investigates the viability of renewable energy for irrigation projects in Saskatchewan. Modelling includes a conventional energization scenario (energy provision through grid interconnection), a combination of conventional and renewable scenario, as well as a 100 % renewable scenario. Further, sensitivity analysis has been undertaken for permitted capacity shortages, utility rates, grid interaction and carbon pricing. The study provides the levelized cost of electricity for each scenario. Baseline results range from $0.0154/kWh for optimised hybrid systems, $0.1429/kWh for grid systems, and as high as $1.1101/kWh 100 % renewable energy systems. The success of renewable energy-driven integration is closely linked to the presence of a grid connection, and the rates governing interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".