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Record W4296755542 · doi:10.55299/jostec.v3i1.55

PLTS Design for Big Industry Needs

2021· article· en· W4296755542 on OpenAlexaboutno aff
Azarya NJ Siahaan

Bibliographic record

VenueJournal of Science Technology (JoSTec) · 2021
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemRenewable energySolar energySoftwareGrid parityElectric powerEnvironmental scienceSolar cellEngineeringGridSolar powerAutomotive engineeringElectrical engineeringProcess engineeringPhotovoltaicsComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Solar cell is a device that converts radiation from sunlight into electrical energy directly, which is also called photovoltaic . The Solar Module ( Photovoltaic ), functions to convert solar energy into DC electric current which is forwarded to the Battery Control Unit (BCU) for further storage in the battery. In this research, a solar cell electric power system with a capacity of 10 MW on-grid will be designed for large industrial needs. The performance of a 10 MW on-grid solar cell power system was simulated using RETScreen Clean Energy Project Analysis software , designed by Natural Resources Canada. This research begins with a prefeasibility study of a 10 MW on-grid solar cell power system using RETScreen software which has an extensive database of meteorological data including daily horizontal solar global radiation as well as databases of various components of renewable energy systems from different manufacturers. The technical and financial performance of a 10 MW on-grid solar cell power system was simulated using RETScreen software. This design is expected to be used as a model to develop a Solar Power Generation System (PLTS) network for large industrial needs.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0360.006

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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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