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Techno-economic Analysis of 100 kWp Floating Solar Photovoltaic for Renewable Energy Mix in Bali – Indonesia

2024· article· en· W4399488239 on OpenAlexaff
Agus Putu Abiyasa, R. Sihombing

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Power Generation
Fundersnot available
KeywordsRenewable energyPhotovoltaic systemPayback periodEnvironmental scienceSolar energyGrid paritySolar powerEnvironmental engineeringInvestment (military)Agricultural economicsMeteorologyEngineeringGeographyProduction (economics)Electrical engineeringEconomicsPower (physics)Distributed generationPhysics

Abstract

fetched live from OpenAlex

Abstract To achieve renewable energy mix target and support the Indonesian Government’s policy on the use of solar power, PT. PLN Indonesia Power Bali has installed Floating Solar Photovoltaic (PV) above the surface of the Muara Nusa Dua reservoir. The 100 kWp Floating Solar PV was showcased at the G20 meeting held in Bali. In this study, a techno-economic analysis was carried out for the 100 kWp Floating Solar PV. The data collected was the real time data from the production of the 100 kWp Floating Solar PV and the costs incurred for the investment. The results of this study showed an average energy yield of 145.93 MWh with expected revenue of Rp 236,406,729.60 annually. Furthermore, the NPV and IRR were calculated and the resulted values were Rp 2,810,168,240.00 and 5.73 %, respectively with payback period approximately of 13 years. This showed that the 100 kWp Floating Solar PV is feasible investment to increase renewable energy mix in Bali – Indonesia.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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