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Record W4390488750 · doi:10.1016/j.ijft.2023.100558

Investigation of Hybrid Renewable Energy Green House for Reducing Residential Carbon Emissions

2024· article· en· W4390488750 on OpenAlexfundno aff
Ezgi Bayrakdar Ateş, N. Karaarslan

Bibliographic record

VenueInternational Journal of Thermofluids · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Analysis and Cultural Studies
Canadian institutionsnot available
FundersYalova ÜniversitesiToronto Metropolitan University
KeywordsRenewable energyGreenhouse gasFossil fuelEnvironmental scienceClimate change mitigationElectricityClimate changeUnit (ring theory)Natural gasEnergy securityPrimary energyEnergy developmentWaste managementEnvironmental engineeringEnvironmental economicsEngineeringEcology

Abstract

fetched live from OpenAlex

In the current era, the effects of climate change are becoming increasingly visible. As a result, the transition from fossil fuels to renewable energy sources has become an inevitable necessity. Renewable energy sources are a crucial tool in reducing greenhouse gas emissions, which are one of the primary causes of climate change. They also support local energy security and reduce the energy dependency of nations. This study presents the design of a hybrid greenhouse that can meet the electricity, natural gas, and fuel needs of a family of four using renewable sources. The HOMER Pro software trial version was used for optimization, and 79,500 simulations were performed, of which 28,360 were applicable. The designed system has a unit energy cost of $0.262 and a unit cost per kilogram of hydrogen of $20.8. Thanks to this study, each house can prevent ∼ 17.82 tons/of carbon emissions per year.

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)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.251
Teacher spread0.220 · 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

Citations22
Published2024
Admission routes1
Has abstractyes

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