Evaluating simplified building models' sensitivity to climate data for energy retrofit optimization
Bibliographic record
Abstract
Accurate yet computationally efficient building energy models are crucial for informed retrofit decisions, particularly in light of climate uncertainty. While the tradeoff between accuracy and efficiency in simplified models has been examined, their performance across diverse climate conditions remains understudied. This paper makes a novel contribution by investigating how weather boundary conditions, including typical meteorological years, extreme events, and future climate projections, affect the accuracy of simplified building energy models. An Ottawa dormitory was selected as the test bed. A high-fidelity whole-building energy model was first built and validated against measured performance. Targeted simplifications in zoning, HVAC, and material properties were then introduced and benchmarked against the validated baseline. Results show that high-abstraction models are more sensitive to weather file selection, with monthly heating errors ranging from 8 % to 22 % in winter months compared to the detailed model. In contrast, finer-resolution models yield more consistent results, with typical monthly errors around 11 %. Simplified models can replicate the detailed model's ability to capture long-term reductions in heating demand under high-emission climate change scenarios. However, short-term performance during transitional months and extreme events reveals larger discrepancies. Such mispredictions are particularly critical when estimating peak loads, where undersized retrofit solutions may compromise resiliency. Overall, findings show that although simplified models may over-select certain retrofit measures, they remain valuable for early-stage analysis and long-term trend assessment, provided that their limitations are recognized and supplemented by higher-fidelity evaluation when necessary.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".