Modelling of a Hybrid-Ventilated Building – Using ESP-r
Bibliographic record
Abstract
This paper reports the results of computer simulations of a hybrid-ventilated building using ESP-r (Environmental Systems Performance for research). A new school building in Norway was used for this simulation study. The research attempted to verify the use of this model for the simulation of a real building utilising hybrid ventilation technologies. The simulations and their analysis focus on the building’s thermal and ventilation performance.The model building has been simulated in three different categories: “building thermal simulation”, “air flow simulation”, and “building + air flow integrated simulation”. Measured weather data were used to define the outdoor conditions of the building.It covers a representative heating period comprising time-series of minutely measured values of air temperature, solar radiation, wind speed, and wind direction. In addition, to assist verification, some measured parameters of the indoor environment, such as temperature, were compared to model predictions.The research concludes that modelling of hybrid ventilated buildings using this type of approach is possible and the resultant outputs are feasible and practical in terms of both the thermal and ventilation points of view. However, at the same time, limitations do exist when it attempts to model complicated building systems or to model CO2 based control systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".