COMPARATIVE SIMULATION OF A HIGH PERFORMANCE BUILDING WITH EE4-DOE2.1E AND ENERGYPLUS
Bibliographic record
Abstract
The University of Calgary’s Child Development Center (CDC) is a 12,000m2 new LEED Platinum building, for which building energy performance in the “very cold ” climate was a key design consideration. A Natural Resources Canada (NRCan) incentive program required that energy modeling be done with NRCan’s EE4 user interface (DOE2.1E simulation engine) and following NRCan procedures. As DOE2.1E lacks specific features to simulate advanced systems such as radiant cooling that were included in the CDC, an EnergyPlus model was later developed to further evaluate these systems. The EE4-DOE2.1E model was used for quality control in EnergyPlus model development. While there were large differences for some components, the whole building energy use estimates were similar with regard to systems for which DOE2.1E had specific simulation features. Advanced energy systems were added to the EnergyPlus model, but the difference in estimated total annual energy use was very small (photovoltaic generated electricity not included). The comparative process revealed numerous input errors in EnergyPlus model and is recommended until a more straightforward EnergyPlus interface is available.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".