Optimizing cooling setpoint using adaptive thermal comfort concept for school building in desert climates under current and future climates
Bibliographic record
Abstract
Purpose This paper aims to compare the impacts of adaptive daily and seasonal cooling setpoints on cooling energy consumption and overheating hours to determine which approach is more effective in a desert climate, develop a methodology that effectively integrates passive strategies with adaptive daily and seasonal cooling setpoint strategies and assess how future climate conditions will impact these strategies in the medium and long term. Design/methodology/approach (1) Integrate adaptive thermal comfort principles into mechanical cooling systems to find the optimized cooling setpoint. (2) Evaluating the optimized cooling setpoints using a mixed-mode operation: In this step, the natural ventilation is activated by opening 40% of the window area when the indoor temperature is higher than 23°C and the outdoor temperature. Both the adaptive seasonal and daily setpoint strategies are evaluated. (3) If overheating hours exceed acceptable limits gradually add mitigation measures (e.g. exterior shading, cool roofs and green roofs). (4) If necessary, further reduce the cooling setpoint until acceptable limits are met. (5) Generate extreme future climate scenarios and evaluate the optimized model. (6) Implement additional measures and setpoint adjustments to maintain acceptable overheating hours in future conditions. Findings Although the building complies with the Dubai Green Code and uses external shading, its cooling energy consumption was 92 kWh/m² in 2021 with a 24°C setpoint. Using the adaptive seasonal setpoint combined with a cool roof, night cooling and cross-ventilation reduces cooling energy consumption by 52, 48 and 35% in 2020, 2050 and 2090, respectively, with overheating hours not exceeding 40 h annually. Using an adaptive daily setpoint strategy with the same mitigation measures is similarly effective; it achieved a 57, 42 and 34% reduction in cooling energy consumption in 2020, 2050 and 2090, respectively, while eliminating overheating hours. Originality/value The originality and value of this study lie in optimizing cooling setpoints without the effect of overheating hours in desert climates. Using the adaptive seasonal setpoint combined with a cool roof, night cooling and cross-ventilation reduces cooling energy consumption by 52, 48 and 35% in 2020, 2050 and 2090, respectively, with overheating hours not exceeding 40 h annually. Using an adaptive daily setpoint strategy with the same mitigation measures is similarly effective; it achieved a 57, 42 and 34% reduction in cooling energy consumption in 2020, 2050 and 2090, respectively, while eliminating overheating hours. Highlights
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".