SIMULATION AND MODEL CALIBRATION OF A LARGE-SCALE SOLAR SEASONAL STORAGE SYSTEM
Bibliographic record
Abstract
The Drake Landing Solar Community (DLSC) is a master planned neighborhood in the Town of Okotoks, Alberta, Canada that has successfully integrated energy efficient technologies with a renewable energy source. The 52-house subdivision has space and water heating supplied by an innovative system which includes solar energy captured by an 800-panel garage mounted array, a borehole thermal energy storage system (BTES) for seasonal energy storage, and short-term thermal storage (STTS) tanks acting as a central hub for heat movement between collectors, district loop, and BTES. The system was designed to achieve a 90% annual solar fraction after 5 years of operation. A computer simulation was completed during the design phase to determine the appropriate sizes of the different components and the expected solar fraction for the project. Since completion of the construction of the subdivision, monitoring data has been collected for the entire system and this data is being used to calibrate the model constructed during the design period. This paper will discuss the original modeling of the solar district system and the calibration of the model with the measured data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".