archetypal: A Python package for collecting, simulating, converting and analyzing building archetypes
Bibliographic record
Abstract
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.067 |
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".