Building Energy and IAQ improvement by Coupled Model
Bibliographic record
Abstract
Abstract The building performances are related to Energy Efficiency and Indoor Air Quality (IAQ). Modeling is one of the best accurate tools for measuring the building performance. Nowadays, Energy Efficiency and IAQ are modeled individually for buildings. Improvement strategies in both areas are analyzed separately. The fundamental problem in Energy and IAQ modeling is related to interaction to each other. This problem makes the modeling results unrealistic to the building performance solutions. To avoid this problem, in this current research, Energy and IAQ models are coupled simultaneously as a new co-simulation method. EnergyPlus and CONTAM are used as Energy and IAQ models, respectively. With the co-simulation method, these two models are coupled together. The method is based on the exchange of control variables between both models dynamically and simultaneously. As a result, the exchanges of temperature and air flow variables are corrected. The verification of the new model is based on the comparison of the simulation and analytical results of temperature and air flow variables. In the next step, this new coupling-co-simulation method for a townhouse building is done in two cases: a leaky and a tight building envelopes. Both cases are compared in two types of ventilation systems: infiltration only, and exhaust only. At this point, the simulated air change rates, gas energy use, and particles concentrations are compared for each case. Finally, the necessity of the accuracy of this new method is concluded.
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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".