Analysis on the driving factors and patterns of window opening and closing behaviour in French households
Bibliographic record
Abstract
Abstract Window operation plays a vital role in indoor environmental quality (IEQ) and building energy consumption while maintaining occupants’ expected IEQ levels. In recent years, the influencing factors of window opening/closing behaviour have been widely investigated and evaluated in residential buildings, aiming at simulating building energy performance in a realistic manner. However, due to the challenges in collecting and analysing occupancy-related data, previous research works emphasized more on indoor/outdoor parameters (e.g. temperature and CO2). Hence, the correlation between occupancy patterns with window behaviour in a household has not been well explored. The aim of this study is to analyse the patterns of window opening and closing behaviour in French households and identify respective driving factors. The analysis was based on the data collected from four apartments in a high-performance residential building located in Lyon, France. The dataset considered in this study includes indoor environment data, weather data and occupancy behaviour with one minute resolution. Both model-dependent and model-independent approaches were adopted to assess the relative importance of driving factors for window operation. The results obtained in this study will provide insights regarding different driving factors for window operation and its related impact on occupant behaviour model performance. The outcomes of this research work can be used as input variables of occupant behaviour models in order to improve the building energy simulation performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".