Recent Inter-seasonal Underground Thermal Energy Storage Applications in Canada
Bibliographic record
Abstract
Canada receives a significant amount of solar radiation compared to other International Energy Agency (IEA) nations. It is important to recognize that from April to September, on average, Canadian cities receive over 90% of solar radiation as in Miami, Florida. However, due to our geographic location and climatic conditions, the solar radiation is more abundant in the summer months and relatively low during the winter season when our energy demand for space heating is at a peak. Underground thermal energy storage (UTES) may be implemented in rocks or soil via a series of vertical borehole heat exchangers or in deep aquifers. This paper will review recent technological advances in the area of high temperature underground thermal energy storage in Canada, including the construction of the first community-scale solar heated, inter-seasonal thermal storage system in Canada. A vast amount of knowledge and experience relating to UTES has been documented. Engineers need to become familiar with this promising technology so that this tool could be made available to business stakeholders in the development of an efficient energy management system. A significant quantity of fossil fuel is required to meet our heating and cooling demand. We have an opportunity to capture the energy potential and utilize the stored energy to displace a large portion of the fuel used for space heating and cooling and make a significant contribution to our greenhouse gas (GHG) emissions reduction goals.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".