Seasonal Thermal Energy Storage in Buried Water Tanks for Single Detached Houses
Bibliographic record
Abstract
Seasonal thermal energy storage (STES) could allow solar energy to offset the majority of building energy loads in cold climates. This thesis outlines one of the first long-term, full-scale experimental studies on seasonal storage at the single-detached home scale. A solar thermal system couples a large evacuated tube solar array to both short term thermal storage tanks and a 36 m 3 buried water tank used for seasonal storage. Solar heat stored in these water tanks provides space heating (SH) and domestic hot water (DHW) to an energy-efficient two-storey research house in Ottawa, Canada. Long term experiments are described, including a one-year cycle of the system and long term heat loss monitoring. Results show that the as-built system can meet the majority of the building's SH and DHW loads, achieving a solar fraction of 68%. However, experiments revealed several areas of underperformance. Most prominently, faulty solar collectors limited the system's potential. To assess the true potential of the system, detailed energy models were developed and validated against experimental data. Simulated free of faults and underperforming components, the system has a predicted solar fraction of over 90%. Building simulation is further used to explore improved control and sizing of STES systems for single-detached homes. Control methods and decisions such as variable speed pumping, radiant floor supply temperature modulation, and storage setpoints are explored, among others. In regard to sizing, for the house under study, it is shown that solar fractions over 90% require relatively large (and potentially costly) STES tanks (>30 m 3 ). However, a moderately lower solar fraction of 70-80% may be obtained even with significantly smaller tanks (<10 m 3 ), provided an "oversized" solar thermal array is utilized, which may come at a significantly lower investment cost.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".