MétaCan
Menu
Back to cohort
Record W3207413166 · doi:10.31963/intek.v8i2.2962

Hybrid Electrical System Optimization of Remote Island using HOMER

2021· article· en· W3207413166 on OpenAlexaff
Adrian Mansur

Bibliographic record

VenueINTEK Jurnal Penelitian · 2021
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsPositive Living North
Fundersnot available
KeywordsCost of electricity by sourceCapital costRenewable energyIntermittencyNet present valueReliability (semiconductor)ElectricityEnvironmental scienceEnvironmental economicsAutomotive engineeringOperating costHybrid systemElectricity generationReliability engineeringBusinessComputer sciencePower (physics)EngineeringElectrical engineeringProduction (economics)MeteorologyEconomicsWaste managementGeography

Abstract

fetched live from OpenAlex

Renewable energy is an important solution in improving access to electricity, especially in isolated areas including the utilization of solar power however intermittency is becoming the main challenge that is faced in the application of Solar Power Plant (SPP). Hybrid system implementation becomes a strategy in an effort to improve system reliability based on the variation in load and intermittency of SPP. Salemo Island is one of the areas that has utilized SPP as one of the sources of electricity energy, but its operation has not been optimal as the load increases, therefore, a study was conducted to optimize the hybrid electrical system based on the current existing conditions. The results of the study showed that with the addition of SPP capacity of 200 kWp and the addition of batteries as much as 120 units, it could obtained an optimal Leveled Cost Of Energy (LCOE) value of 1,306 IDR/kWh and operating costs of 450 million with a Net Present Cost (NPC) of 8.4 billion and a capital cost of 3.2 billion. Another option is the addition of SPP 150 kWp with a lower capital cost value of 2.02 Billion, but greater than the aspect of operating cost of 883 million and LCOE of 1,897 Rp/ kWh

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueINTEK Jurnal PenelitianSame topicHybrid Renewable Energy SystemsFrench-language works237,207