Is Typical Year weather data still appropriate for Building Energy Simulation? A comparative analysis on the traditional practice of using Typical Year weather data and a modern approach in using Historical weather data in Building Energy Simulation
Bibliographic record
Abstract
This research compares the use of Typical Year weather files (CWEC1990s and 2016) and Multi-Year Historical weather data (CWEEDs1998 to 2014) in Building Energy Simulation. The analysis is comprised of several components: 1) a statistical analysis on the raw weather elements - differences in the cumulative distribution of key weather elements, and relevant weather indices are compared; 2) analysis on the differences in heating and cooling energy estimations; 3) analysis on the impact of the staggered update cycles between weather data used for equipment sizing and simulation. Computer scripts created to automate tasks related to multi-year simulation are also presented. It is found that critical insights are missing when Typical Year weather files are used for simulations. Simulation with Multi-Year Historical data shows potential as a reasonable alternative, since they can be updated more frequently and contain a wider range of conditions. Repetitive tasks can also be automated with scripts.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".