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Record W4406128748 · doi:10.18280/jesa.570611

Influence of Building Electric Demands on Performance of a Vertical Axis Micro Wind Turbine in Italy: Energy, Environmental and Economic Numerical Assessment

2024· article· en· W4406128748 on OpenAlexvenueno aff
Antonio Rosato, Achille Perrotta, Luigi Maffei

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerTurbineVertical axisVertical axis wind turbineEnvironmental scienceEnergy (signal processing)Aerospace engineeringEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Vertical axis small-scale wind turbines are gaining popularity because of their capacity to generate electricity from a renewable source by using wind from all directions.In this study, the performance of a commercial Savonius vertical axis micro wind turbine with a rated maximum output of 2200 W have been analyzed through the modeling and simulation environment TRNSYS by varying the served building while installed into 5 different Italian cities.In particular, three typical different building typologies (a singlefamily dwelling, a small district consisting of 5 single-family dwellings, as well as an office) have been considered and the corresponding electric demands have been developed via an innovative stochastic approach.The climatic conditions have been taken into account by means of detailed weather data files.The building-integrated wind turbine's performance has been contrasted with a reference scenario that corresponds to the same building but uses the central electric grid exclusively.The comparison has been carried out from an energy, environmental, and economic perspective.The findings of simulations indicate that using the wind turbine can cut down the amount of electricity purchased from the central electric grid up to 37.51%, the global equivalent carbon dioxide emissions up to 37.74% and the operating costs up to 85.93%, with a minimum simple pay-back period of 1.09 years.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.585

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 head0.006
GPT teacher head0.225
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

Citations1
Published2024
Admission routes1
Has abstractyes

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