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Record W7043537717

Studying the Influential Parameters of an Office Building’s Energy Consumption in North America

2023· article· en· W7043537717 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2023
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy consumptionConsumption (sociology)Range (aeronautics)Energy (signal processing)ElectricityPopulationParametric statisticsSensitivity (control systems)
DOInot available

Abstract

fetched live from OpenAlex

The study of building energy consumption has gained immense significance in recent times due to the burgeoning global population and the rapid depletion of energy resources. The present research focuses on analyzing individual parameters that impact building energy usage and devising methods and strategies to reduce energy consumption. An existing office building in Philadelphia was chosen as a reference for simulation in TRNSYS. The factors that affect the building, such as ambient temperature, solar radiation, building envelope, wind speed, and internal gains, were studied and defined according to the existing building standards. Predictive modeling is performed with these inputs for a range of infiltration rates – 0.25 ACH to 0.85 ACH, considering the variability of the parameter. The validated model was subjected to a sensitivity analysis by changing one potential parameter at a time to examine the influence of variation of these parameters on energy usage. The analysis found that the highest energy reduction is executed by replacing double-glazing windows with triple-glazing, with an energy saving of 8.43%. To evaluate the effect of location, a similar sensitivity study is conducted for the same office building in Edmonton and Mexico City. It is found that by replacing the same triple-glazing window with double-glazing, a 12.3% and 5.44% energy saving is achieved for the building in Edmonton and Mexico City, respectively. Henceforth, depending on electricity prices for the respective cities, building in Philadelphia, Edmonton and Mexico City is found to have a monthly savings of $3,133, $7582, and $1,552, respectively (all $ in USD). When considering identical parametric inputs, distinct energy savings are observed across varying locations. These statistics serve as valuable tools for making well-informed and rational decisions regarding investments in energy-efficient technologies and the pursuit of Net Zero energy buildings.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.208
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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