Modeling photovoltaic solar farm site suitability using a multi-criteria evaluation in Southern Ontario, Canada
Bibliographic record
Abstract
Canada’s solar energy industry has seen significant growth in recent years. More specifically, southern Ontario, Canada’s most densely populated region, hosts over 100 industrial-scale solar farms. These large arrays of interconnected solar panels convert incoming global horizontal irradiance into electricity. This study is a location-based analysis investigating the suitability of southern Ontario’s lands for hosting solar farms. The objectives are: (1) determine the most applicable variables and methods that are replicable in the southern Ontario region; (2) identify and analyze the amount and location of suitable areas; and (3) understand and explain any discrepancies between the locations of existing solar farm power plants and optimal locations. This research produces a land suitability model of southern Ontario’s solar farm capacity. It utilizes an analytical hierarchy process, a type of multi-criteria evaluation, in order to identify criteria, classify and weigh variables, and overlay inputs. The data employed are incoming solar radiation, cloud cover, land use, hydro transmission lines, major road networks, slope, and cultural areas. The results suggest much of the northern portions of southern Ontario are not well adapted for hosting solar farms, while large parts of southwest and west-central are very suitable or mostly suitable. In particular, a west-central area remains untapped for solar farming, despite being highly suitable. These results are then compared to the approximate locations of existing solar farms, and discrepancies are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".