A novel framework for Bayesian calibration of building energy models with sub-hourly building operational data
Bibliographic record
Abstract
Building energy simulation models are useful for optimizing building systems, detecting and diagnosing faults, and making retrofit decisions. However, these models need to be calibrated with building data to accurately represent building systems. Bayesian calibration approaches have been developed to address the uncertainties in building behavior, but most of them are designed for monthly or yearly operational data. In this study, we propose a novel framework for Bayesian calibration of building energy models using sub-hourly building operational data. The framework employs parametric modeling methods for surrogate modeling and model inadequacy representation to improve computational efficiency and enable calibration with large time series datasets. In addition, it uses variational inference for the estimation of the posterior distribution over uncertain parameters. Our simulation study shows that the proposed Bayesian calibration framework provides reliable and cost-effective calibration of a building energy simulation model with a large time series dataset. With the proposed framework, it is expected that users can have well-calibrated detailed building models that can be used for whole building level problems (e.g., M&V, retrofit) as well as subsystem level control and maintenance with a proper level of risk assessment.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".