A Case Study on the Energy Efficiency of Windows in Institutional and Residential Buildings
Bibliographic record
Abstract
Building energy consumption occupies an increasing proportion of the total energy consumption of society, and the use of energy-efficient windows can have great significance for energy saving. This case study examined the energy efficiency of various types of windows of the buildings on the University of New Brunswick campus, Fredericton, Canada. The energy performance of these windows was monitored by an infrared thermal camera from November 2021 to April 2022 and assessed in terms of the heat loss between different types of windows. The main findings were that (1) the temperature distribution of a window was strongly influenced by the indoor and outdoor temperature; (2) wood frame windows showed better insulation properties than metal frame ones; (3) fixed windows had a better energy performance than sliding windows and single-hung windows; and (4) the east orientation of a building and the use of Low-E glazing were the most effective expedients to reduce the winter energy required. By comparing these findings with earlier research, this study contributes new insights for cold climates, underscoreing the importance of Low-E glazing and configuration choice in building retrofits for energy-efficient, sustainable construction.
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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".