Assessing the Energy and Indoor Air Quality Performance for a Three-Story Building Using an Integrated Model, Part One: The Need for Integration
Bibliographic record
Abstract
In building applications, there is a dynamic interaction/coupling between the energy performance and the indoor air quality (IAQ) performance. Previously, the performance of energy consumption (EC) and IAQ has been evaluated independently. In this study, an energy performance model (EnergyPlus) and IAQ performance model (CONTAM: contaminant transport analysis) were simultaneously coupled as a new integrated simulation model in which the control variables were exchanged between the two models. Two scenarios were provided in this study for a three-story house. The first scenario addressed the effect of airtightness only. The second scenario, however, addressed the airtightness with an exhaust fan with an upgraded filter. In order to better analyze the accuracy of the simulations, the performance of the energy and IAQ were simulated independently using the EnergyPlus model and CONTAM model. Thereafter, the performance of the energy and IAQ were simulated using the present integrated simulation model. All simulations were conducted for the climatic conditions of Montreal and Miami. The results of the integrated simulation model showed that the exchange of control variables between both EnergyPlus and CONTAM produced accurate results for the performance of both energy and IAQ. Finally, the necessity of using the present integrated simulation model is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".