Performance Evaluation of Ground-Coupled Seasonal Thermal Energy Storage with High Resolution Weather Data: Case Study of Calgary Canada
Bibliographic record
Abstract
The interest in harnessing renewable sources of energy for space heating in residential applications has increased in recent decades due in part to their cost benefits and an increase in environmental awareness. The proposed system uses shallow ground as an energy reservoir; able to absorb heat during the summer and release it during the winter respectively. This paper presents by means of a mathematical model an analysis of such a system using real temperature profiles and compares them to idealized fitted functions. A validated two-dimensional multiphase model describing mass, momentum, turbulence and conjugate heat transfer between the bayonet tube and the ground is used to compare the effects of using a simplified fitted function to represent the ambient temperature with that of hourly temperature readings from a weather monitoring station. The results indicate that the strong random nature of the temperature variations complicates the analysis. It is shown that fitted functions can over predict the overall performance of the system, however under-predict the performance on the short term due in part to cold snaps, heat waves and variations in temperatures. While the benefits and capabilities of the system under real loads show the potential of the system, work on thermal buffering or real-time intelligent control systems for quality control will be necessary to maintain constant temperature for thermal comfort and optimum energy extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".