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Record W4414554966 · doi:10.3390/buildings15193478

A Case Study on the Energy Efficiency of Windows in Institutional and Residential Buildings

2025· article· en· W4414554966 on OpenAlexafffundabout
Meng Gong, Alireza Kaboorani

Bibliographic record

VenueBuildings · 2025
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsRed River CollegeUniversity of New Brunswick
FundersNew Brunswick Innovation Foundation
KeywordsGlazingEnergy consumptionEfficient energy useWindow (computing)Energy (signal processing)Energy performanceThermal management of electronic devices and systemsTotal energy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.219
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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