Optimising a solar-based microgrid system for urban areas: a case study in Edmonton
Bibliographic record
Abstract
Nowadays, the transition from fossil fuels to renewables and solving renewable energy challenges such as their oscillating nature has become necessary. Here, the building energy model of a residential area in Edmonton, Canada, is formulated using computer tools to provide this area's required power through solar energy optimally. The meta-heuristic approach is utilised to address and optimise the proposed system performance. The results of modelling and optimising system performance for two scenarios (free and prohibited sale of generated electricity to the power grid) are reported. The most striking result is that the PV panel capacity is 756 kW, but the total area required to install these panels was 4458 m2. The total electricity produced by photovoltaics on a summer day in the base scenario was 685 kWh, and the total energy purchased from the grid was 995 kWh. These values for the winter day were 212 and 1496 kWh, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".