Revised heating degree days due to global warming for 15 major cities of South Korea
Bibliographic record
Abstract
Because of the rapid rise in ambient temperatures in urban cities due to global warming, this research study was conducted to revise the heating degree days (HDDs) for main cities of South Korea. Current HDDs used in the design of heating systems were established some 30 years ago. Therefore, there is a need to revisit and revise the HDDs used in Korea. The HDDs were computed at five different indoor set-point and unloaded temperatures. The validity of the methodology used for computing HDDs was ascertained by comparing the calculated HDDs with the published values. The impact of the length of time on total annual HDDs was examined. The results show that higher temperature trends due to global warming witnessed over the past decade in general decreased the HDDs. The impact was higher for warmer climate cities than the cold regions. The revised annual HDDs for 15 major cities of South Korea are presented in this paper. Practical applications: The HDDs corrected for global warming effects for 15 major cities of South Korea presented in this article are useful for designers in estimating the impact on equipment size and energy consumption. Towards this end, several scenarios of global warming effects are presented by assuming several unloaded temperature levels. This is useful for the designers in examining the uncertainties in the estimation of energy consumption. The results published are also important for policy makers in South Korea to examine the need for revising the degree day database in light of the global warming trends.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".