Bibliometric Review of Passive Cooling Design Strategies and Global Thermal Comfort Assessment: Theories, Methods and Tools
Bibliographic record
Abstract
Globally, a variety of factors, ranging from ethnicity and occupants’ lifestyles to the local climate characteristics of any studied location, as well as people’s age, can affect thermal comfort assessments. This review paper investigates the energy effectiveness of state-of-the-art passive systems in providing neutral adaptive thermal comfort for elderly people by exploring passive design strategies in four distinct climates, namely Canada, India, Abu Dhabi and the Eastern Mediterranean basin. The aim of the study is to analyse the available data provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers’ (ASHRAE) Global Thermal Comfort Database II, version 2.1. The main objective of the study is to develop an effective methodological framework for the on-going development of adaptive thermal comfort theory. To this extent, this study presents a comprehensive review of the assessment of energy effectiveness of passive design systems. To accomplish this, the impact of climate change factors in passive design systems was investigated. A meta-analysis method was adopted to determine the input variables for the statistical analysis. Cramer’s V and Fisher’s Exact tests were used to assess occupants’ thermal sensation votes (TSVs). The findings revealed that there are discrepancies detected between the in situ field experiments and the data recorded in the ASHRAE Global Thermal Comfort Database II. The study findings contribute to the development of adaptive thermal comfort theory by reviewing the existing methodologies globally. Furthermore, a critical review of the significance of occupants’ age differences should be conducted in the identification of neutral adaptive thermal comfort.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.075 | 0.098 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".