Bibliographic record
Abstract
Traditionally, hourly weather years such as the test reference years (TRYs) and design summer years (DSYs) have been used for building energy and thermal performance analysis. Until recently, these weather datasets were based on observed measurements, but the need to adapt buildings to the impacts of likely future climate change has introduced a requirement to incorporate climate projections, such as the UK Climate Projections (UKCP09), into building performance analysis. Four research projects, funded by the EPSRC, examined the use of UKCP09 data, and the associated Weather Generator tool, in producing weather files appropriate for building simulation. A methodology called ‘morphing’, previously used to create the currently available to practitioners, UK Climate Impacts Programme (UKCIP02) based, CIBSE Future Weather Years, will also be discussed here as a potential alternative for the production of UKCP09-based weather files. This article reviews all above methodologies developed to produce weather files for building simulation, using the UKCP09 projections, and discusses their benefits and limitations as well as their ease of use by designers. Practical application: This article aims to provide a comprehensive review of the various methodologies currently available for the production of future weather files for building thermal and energy performance simulation using the UKCP09 projections. This analysis aims to provide users with the benefits and limitations associated with each methodology and end product based on their accessibility, consistency with other currently used datasets, computational resources required and spatial availability.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".