Guidelines for low energy building design based on the adaptive thermal comfort concept - Technical report: IEA EBC Annex 69: Strategy and Practice of Adaptive Thermal Comfort in Low Energy Buildings.
Bibliographic record
Abstract
The adaptive thermal comfort concept has been developed over many years and proven in numerous field studies (e.g. Webb 1964, Nicol and Humphreys 1973, Auliciems 1981b, de Dear et al. 1997, McCartney and Nicol 2002, Manu et al. 2016), showing that people are satisfied with a wide range of thermal conditions. Prerequisite is that people are provided with means to make themselves comfortable, that they know which opportunities they have, that it is socially acceptable to use these opportunities and that they are willing to use them (Hellwig, 2015). However, the overall understanding of how to design for such opportunities enabling the occupant to make themselves comfortable in relation to climate and building type, thus how to convert the adaptive thermal comfort concept into building design and concepts for operating buildings, is still limited. There are still common misunderstandings in the interpretation of the adaptive comfort approach among building planners and operators e.g. regarding the amount of control, the seriousness of this topic or the level of information needed by occupants for which reason guidance (e.g. CIBSE 2010, Cook et al. 2020) and knowledge transfer (e.g. Hellwig and Boerstra 2017, 2018) is absolutely essential. Consequently, there is still a gap between scientific research and real-world-application, which this report aims to diminish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.024 |
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".