The development and validation of a furnace model for ESP-r/HOT3000
Bibliographic record
Abstract
The validation approaches for algorithms dealing with heating ventilating and air conditioning (HVAC) systems was examined. A fuel-fired furnace model was incorporated into the ESP-r/HOT3000, the next generation HOT2000 simulation project at the CANMET Energy Technology Centre of Natural Resources Canada. The development process for the model involved the selection of an appropriate algorithm which considers coding standards. The furnace model is an empirical-based model which was validated through a series of IEA BESTEST fuel-fired furnace validation test runs in 3 whole-building simulation programs. ESP-r applies a finite-difference formulation based on a control-volume heat-balance to represent all relevant energy flows within the building. Finite-difference nodes are used to represent rooms, the internal and external surfaces of walls and windows, as well as boilers and ducts. An algebraic heat balance was written for each node, indicating the governing partial differential equations and linking all inter-node heat flows over time and space. Very good agreement was found between the calculated solution and simulation results. The system for testing furnace algorithms can be readily applied to other HVAC system models including air-source heat pumps, but as the complexity of the algorithms increase, there will be more differences between simulation engines. 11 refs., 4 tabs., 6 figs.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".