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Record W4256701602 · doi:10.22215/etd/2020-14434

Techno-Economic Analysis of a Solar Adsorption Cooling System for Residential Applications in Canada

2020· dissertation· en· W4256701602 on OpenAlexafffundabout
Jordan McNally

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFossil fuelRenewable energyGreenhouse gasEnvironmental scienceEnvironmental engineeringCarbon dioxidePopulationNatural gasSolar energyGridGrid systemWaste managementEngineeringGeographyChemistryEcologyElectrical engineering

Abstract

fetched live from OpenAlex

As the population increases so does the demand for space cooling which causes higher peak loads on the electrical grid. Areas without a renewable dominant energy grid produce more carbon dioxide during peak periods due to the fossil fuel plants ramping up to match demand. Solar adsorption cooling reduces and shifts the electrical loads required for cooling. Various residential applications of an adsorption system were studied within this project for different cities across Canada. Regions in Ontario would benefit the most due to higher solar potential for water heating and the reduction of fossil fuel plants. Areas where natural gas is available for a low cost, like Alberta, were found to have a much lower economic benefit for this type of system but would receive a massive reduction in carbon dioxide emissions. This study produced promising results depending on the GHG composition of the electrical grid, utility rates, and weather.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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 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

Citations6
Published2020
Admission routes3
Has abstractyes

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