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Record W4405107916 · doi:10.3390/engproc2024076094

Review of Techno-Economic Analysis Studies Using HOMER Pro Software

2024· article· en· W4405107916 on OpenAlexaff
David Ross-Hopley, Lord Ugwu, Hussameldin Ibrahim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsRenewable energyEnvironmental economicsEconomic analysisSoftwareGlobeEngineeringNatural resource economicsComputer scienceEconomicsAgricultural economicsElectrical engineering

Abstract

fetched live from OpenAlex

With decreases in cost accompanying advances in technology, renewable energy is becoming increasingly viable. Much software is available for the techno-economic analysis of energy systems, and HOMER Pro software is frequently applied for micro-grid and industrial analysis. Around the globe, techno-economic analyses of a variety of renewable energy systems have been undertaken using HOMER Pro Version 3.16.0. This study reviews the primary techno-economic findings of past research to investigate recent trends. Based on high-level trendline analysis, it appears that the costs of renewable energy systems have decreased in academic HOMER Pro-based literature. Of the articles analyzed, the LCOEs for 100% renewable energy systems have decreased from $0.91/kWh to $0.70/kWh, while the LCOEs for 0% renewable energy systems have increased from $0.74/kWh to $0.78/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 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)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0140.020
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.049
GPT teacher head0.333
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2024
Admission routes1
Has abstractyes

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