archetypal: A Python package for collecting, simulating, converting and analyzing building archetypes
Bibliographic record
Abstract
<pre>The field of Urban Building Energy Modeling (UBEM), which assesses the energy performance of buildings in cities relies on advanced physical models known as building energy models that are representative of the building stock. These building archetypes are often developed in specific modelling platforms such as EnergyPlus or TRNSYS, two leading simulation engines in the field of building energy modeling. EnergyPlus is an open source simulation engine developed by the US Department of Energy. TRNSYS is a well established and specialized simulation platform used to simulate the behavior of transient systems. The Urban Modeling Interface (UMI), developed by the MIT Sustainable Design Lab, leverages EnergyPlus to enable building energy modeling at the urban scale. The three tools offer many advantages in their respective fields, but all suffer from the same flaw: creating building archetypes for any platform is a time-consuming, tedious and error-prone process. `archetypal` is a Python package that helps handling collections of such archetypes and to enable the interoperability between these energy simulation platforms to accelerate the creation of reliable urban building energy models. This package offers three major capabilities for researchers and practitioners: 1. Run, modify and analyze collections of EnergyPlus models in a persistent environment; 2. Convert EnergyPlus models to UMI; 3. Edit UMI Template Files in a scripting environment; 4. Convert EnergyPlus models to TRNSYS TrnBuild Models. </pre>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".