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Record W2007762622 · doi:10.1504/ijex.2013.056132

Energetic and exergetic assessment of solar and wind potentials in Europe

2013· article· en· W2007762622 on OpenAlexaff
Olivier Le Corre, Jean Sébastien Broc, İbrahim Dinçer

Bibliographic record

VenueInternational Journal of Exergy · 2013
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsRenewable energyWind speedExergyEnvironmental scienceMeteorologyWind powerExergy efficiencyAtmospheric sciencesEngineeringGeographyProcess engineeringPhysics

Abstract

fetched live from OpenAlex

This paper deals with a physics–based assessment of renewable energy potential in Europe, particularly solar and wind energy sources, using two literature models. A sensibility analysis with the weather data is first done. Actual temperature, pressure, relative humidity, global radiation and wind speed data are employed to develop energy and exergy maps for Europe, based on iso–areas of land–use. These maps are compared with similar existing ones. A good agreement is obtained. A paradoxical result is found for wind exergy efficiency. The yearly average exergy efficiency where wind speed is less than 5 m/s is greater than that where wind speed is greater than 7 m/s. This can be explained by the 'dome' shape of wind exergy efficiency. A solar efficiency map for Europe is also developed to serve as a useful guide for choosing a renewable energy form based on yearly energy production.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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