Estimating thermal energy loads in remote and northern communities to facilitate a net-zero transition
Bibliographic record
Abstract
Abstract Canada has more than 350 remote and northern communities, most of which rely on diesel for their electric and thermal needs. This reliance is deleterious to climate, health, albedo, and energy security—all diesel must be imported. The government is working to transition these communities to climate-friendly and sustainable alternatives, but assessments of this transition are hampered by limited data availability, especially the absence of hourly thermal load profiles. Here, we develop a method for estimating the thermal load profiles of these communities; apply it to 40 communities that vary across characteristics like population, location, accessibility, and Indigenous identity; and seek to validate these profiles with the few empirical data that exist. We also develop a model to predict the thermal load of a remote and northern community using limited, available information like population and location. This paper represents the first attempt to simulate hourly thermal load profiles for these communities. We find that thermal loads are large—the hourly thermal load can be up to 23 times the hourly electrical load in winter, which has implications for investment planning. Our research helps communities, investors, and analysts develop robust transition plans as they seek to decarbonize northern communities’ energy systems.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".