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Record W2988407478 · doi:10.32438/wpe.3019

A comparative study of a direct current heating system and a gas furnace heating coil

2019· article· en· W2988407478 on OpenAlexaffabout
Ali Taileb

Bibliographic record

VenueWEENTECH Proceedings in Energy · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsDurham College
Fundersnot available
KeywordsHeating elementHeating systemCurrent (fluid)Direct currentElectromagnetic coilEnvironmental scienceElectricityNuclear engineeringEngineeringElectrical engineeringMechanical engineeringProcess engineeringVoltage

Abstract

fetched live from OpenAlex

The objective of this research is to compare the efficiency of a direct current (DC) heating system with an electrical furnace coil. This was done using a house lab as a test bed located on the Durham College Whitby Campus in Canada. The house is approximately 1000 square feet, originally built circa 1950's/1960's, with an existing gas furnace of 60,000 BTUs and an energy efficiency EnerGuide rating of 95.5. Three options were tested during winter 2016 along with the electrical heating element. Weather normalization was taken into consideration using data provided by the weather network. The analysis showed that the direct current heating system option 3 had a higher BTUH/Watt= 3.73 compared to the furnace heating element=2.25 BTUH/Watt which represent a difference of 39.6%. An energy simulation was run using the HOT2000 software to evaluate the direct current technology vs gas, oil, propane and electricity. In each case the direct current technology showed an energy savings better than the comparative technology. From the data collected and analysis, it can be concluded that the direct current system is a valid technology for heating buildings. It is more efficient than the industry standard electrical heating coil with an efficiency of up to 40% better.

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 score0.051
Threshold uncertainty score0.698

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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