Cooling Degree Days for Quick Energy Consumption Estimation in the GCC Countries
Bibliographic record
Abstract
One of the most useful and simplified approaches in assessing building energy estimates is the degree days method. The heating and cooling requirements can be easily compared for different locations as well as system trends. In this paper, the cooling degree day values for the capitals of the Gulf Cooperation Council (GCC) are presented. Degree day values at different base temperatures are also produced for these locations. These values are useful for engineers and policy makers for evaluating energy demands and their cost for these countries. A typical two-story residential building is considered here and its yearly cooling load is evaluated. The total cooling energy is compared based on the energy cost of the respective GCC countries. The results presented in this investigation illustrated that the cooling load, based on the cooling degree days (CDDs) at a 23 °C base temperature, agrees well with the detailed hourly cooling load simulated by eQuest software. Additionally, the highest CDDs value of 2589 was observed in the city of Doha and the lowest value of 2037 was seen in Riyadh city. The lowest cooling cost of USD 492 corresponds to Muscat, while the highest value of USD 1672 belong to Abu Dubai, partially due to a higher tariff of 0.081 USD/kWh.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".