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Record W3138161113 · doi:10.18280/ejee.230102

Real Time Load Assessment and Economic Analysis of RES System

2021· article· en· W3138161113 on OpenAlexvenueno aff
Gaurav Chauhan, Sakshi Bangia

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2021
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyWind powerEnvironmental scienceFossil fuelSolar powerGridWork (physics)Electric power systemEnvironmental economicsSolar energyElectricity generationDistributed generationComputer scienceMeteorologyPower (physics)EngineeringElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Renewable resources are complementary in nature so the weaknesses of one can be overcome with the strength of other. The factors such as climate condition and weather are unpredictable, more for wind than compared to solar sources. The complementary nature of wind and solar sources motivates us to hybrid wind-solar power plant concept. The major problems like environment hazards, depletion of fossil fuels can be overcome by using hybrid power station. With the help of grid interconnection, the energy can be supplied to the remote rural areas and the emission of carbon and other harmful gases can be reduced up to 80% to 90%. This paper focuses on the optimal combination of renewable energy resources that could electrify the area under study. This paper also highlights the village details, energy resources available in region under study and the predicted load assessment. The various power output equations of wind power and solar power has also been discussed. The overview of the Homer software that was used for simulation purposes has also been discussed in this work.

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)0.001
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 score0.231
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.219
Teacher spread0.213 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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