Encapsulation of validation tests in the ESP-r simulation program
Bibliographic record
Abstract
It is well recognised that validation of dynamic building simulation programs is a long-term complex task. There have been many large national and international efforts that have led to a well-established validation methodology comprising analytical, inter-program comparison and empirical validation components, and a significant number of tests have been developed. As simulation usage increases, driven by such initiatives as the European Energy Performance of Buildings Directive, such tests are starting to be incorporated into national and international standards. Although many program developers have run many of the developed tests, there does not appear to have been a systematic attempt to incorporate such tests into routine operation of the simulation programs. This paper reports work undertaken to address this deficiency. The paper summarizes the tests which have been applied to the simulation program ESP-r. These tests have been developed within the International Energy Agency Annexes, within CEN standards, within various largescale national projects, and by the Chartered Institution of Building Services Engineers. It will then describe work undertaken to encapsulate validation tests within the simulation program. The resulting structure allows developers to ensure that recent code modifications have not resulted in unforeseen impacts on program predictions, and allows users to check for themselves against benchmarks.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".