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Development of economic processes utilizing solar energy

2022· article· en· W4318618078 on OpenAlexaff
Kalyan Kumar

Bibliographic record

VenueActa Academiae Beregsasiensis Economics · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsNickel Institute
Fundersnot available
KeywordsEnvironmental economicsRenewable energyEnergy supplyNational GridGridBusinessEngineeringEconomicsEnergy (signal processing)Electrical engineering

Abstract

fetched live from OpenAlex

The concern of the electric utility companies to prioritize tight control on system performance is addressed in the paper as it immensely helps planning maintenance expenditure economically with customers’ expectations fully met. . The paper aims at making a comparative study of performance of the micro-grid power system with that of the interconnected micro-grid and national grid. A micro-grid that utilizes solar energy in sparse locations in hilly terrain for ensuring reliable electric supply to industrial houses engaged in product manufacturing processes has been considered. These manufacturing processes as a part of small and medium industries introduce goods and services necessary for securing economic growth and development in the country side where grid power supply is subject to frequent weather disturbances, and hence, very costly from the perspective of sustenance as well as maintenance. The solar energy trend has given scope for self-reliance and better livelihood of people who live away from cities but the products what they manufacture may be sent for consumption in the nearby cities. Thus, more and more dependence on the solar energy leads to nation’s economic growth policy. Moreover, it adds to uninterrupted and quality electric supply using clean energy in place of fossil fuels-based generation of electricity to protect environment; allows reducing health hazards and mitigating greenhouse gas emissions. Using reliability techniques, solar energy plant performance is quantitatively judged and compared with respect to the performance of the national grid supplying power supply to the remotely located population. The improved energy security, easy access and operational responses of the solar plant as a substitute to the grid power supply help develop economic growth of a nation.

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 categoriesMeta-epidemiology (narrow)
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 score0.768
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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.

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
Published2022
Admission routes1
Has abstractyes

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