Impact of Modeling Simplification on Energy Simulation Speed and Accuracy Considering Climate Change: A Case Study of a Dormitory Building
Why this work is in the frame
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Bibliographic record
Abstract
This study explores the impact of various weather files on the simulation accuracy of building energy model simplifications, focusing on thermal zone abstraction, HVAC system simplification, and material definition. Using a Canadian dormitory building as a case study, a detailed model is progressively simplified into four scenarios, ranging from individual to single-zone models. After calibration with real-world data, both detailed and simplified models are used to evaluate sixteen retrofit scenarios under current and future weather conditions, incorporating various present and future Typical Meteorological Year (TMY) and Canadian Weather Year for Energy Calculation (CWEC) files to account for climate change impacts. Results reveal that all pre-calibrated simplification scenarios demonstrate deviations in total energy demand, approximately below 25%. Moreover, after implementing Energy Efficiency Measures (EEMs), calibrated simplified models exhibit an error of less than 10% compared to the detailed model. Furthermore, the simulations using various weather files reveal trends associated with climate change. As temperatures are projected to rise in the following decades, the simulated models using future weather files show approximately a 30% reduction in heating demand. However, simulations using TMY weather files indicate an average of 8.7% higher natural gas demand compared to those using CWEC weather files. This discrepancy underscores the critical importance of selecting appropriate weather datasets in energy modeling, as varying climatic assumptions influenced by climate change can significantly impact the results.
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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 | 0.000 | 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 it