A Study on An Integrated GEO/TES with Geothermal Heat Exchanger and Thermal Ice Storage
Bibliographic record
Abstract
Peak cooling load of large buildings is generally greater than their peak heating load. Internal and solar heat gains are used fur selection of adquate equipment in large building in cold winter climate like Canada and even Korea. The cost of geothermal heat exchanger to meet the cooling loads can increase the initial cost of ground source heat pump system to the extend less costly conventional system often chosen. Thermal ice storage system has been used for many years in Korea to reduce chiller capacity and shift Peak electrical time and demand. A distribution system designed to take advantage of heat extracted from the ice, and use of geothermal loop (geothermal heat exchanger) to heat as an alternate heat source and sink is well known to provide many benifits. The use of thermal energy storage (TES) reduces the heat pump capacity and peak cooling load needed in large building by as much as 40 to with less mechanical equipment and less space for mechanical room. Additionally TES can reduce the size and cost of the geothermal loop by 1/3 to 1/4 compared to ground coupled heat pump system that is designed to meet the peak heating and cooling load and also can eliminate difficuties of geothermal loop installation such as space requirements and thermal conditions of soil and rock at the urban area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".