IBPSA Modelica Working Group: Open-source model development based on open standards to accelerate decarbonization
Bibliographic record
Abstract
In 2022, the IBPSA Board of Directors approved the formation of the IBPSA Modelica Working Group, https://ibpsa.github.io/modelica-working-group/. Its purpose is to further develop the Modelica IBPSA Library, and to coordinate the needs of the IBPSA community with the Modelica community using the earlier work of IBPSA project 1 and IEA-EBC Annex 60 as a starting point.This paper gives an overview of the Modelica IBPSA Library (https://github.com/ibpsa/modelica-ibpsa), an open-source, free library of component models for building and district energy systems that is implemented in the Modelica language, an open-standard language for modeling of engineered systems. The paper describes the main recent developments of models for heat pumps, geothermal borefields, aquifer thermal energy storage systems, reduced-order building models, ground-coupled district network pipes, controls modeling based on the emerging ASHRAE Standard 231P, and electrical system simulation. It explains how the library is developed and validated, and how it is being used by the four Modelica libraries that use the Modelica IBPSA Library as its core, namely the AixLib, Buildings, BuildingSystems and IDEAS libraries. Modelica uniquely enables cross domain simulations that can couple electrical, fluid, thermal and other types of models. The paper will close with brief examples that show the range of applications supported by these four libraries that integrate some of the newly developed models.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.024 |
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".