Comparative analysis of uncertainty characterization methods in urban building energy models in hot-arid regions
Bibliographic record
Abstract
The development of reliable building energy models at the urban scale is crucial for analyzing and optimizing the energy efficiency of cities. The bottom-up physics-based approach has been widely employed in Urban Building Energy Models (UBEMs). However, the uncertainty of input parameters can impact the reliability of UBEM simulation outputs, and very limited studies considered the uncertainty when developing archetype models for UBEMs. While UBEMs typically rely on a traditional deterministic approach, incorporating probabilistic methods can significantly enhance simulation accuracy by accounting for uncertain variables. Probabilistic methods involve characterizing key uncertainties in input data using Probability Distribution Functions (PDFs). Yet, the effect of using different PDF types on UBEM results is not adequately understood, and the literature often assumes uniform distribution. In this study, UBEM is characterized based on three methods. The deterministic approach is used to serve as a baseline, and two different PDF types are used to examine how PDFs impact simulation results when uncertain parameters are present in UBEMs. Latin Hypercube Sampling (LHS) is employed to propagate uncertainty in input parameters in UBEM. The study is conducted on a case study area of the Marina district of Lusail City, Qatar, characterized by a hot and arid climate.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".