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Record W4404640511 · doi:10.25729/esr.2023.02.0001

Cost Analysis of an Optimized Hybrid Energy System for a Remote Area in St. John’s, NL

2023· article· en· W4404640511 on OpenAlexaffabout
T. Helan Vidhya, Faiyaz S.Al., Manjaiah D. H, Hossain M.A.

Bibliographic record

VenueEnergy Systems Research · 2023
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEnergy (signal processing)Cost analysisComputer scienceTelecommunicationsReliability engineeringEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

In the current energy field scenario, hybrid energy, combining both conventional and non-conventional energy, plays a significant role. With the development of technology, this new energy category has gained popularity due to its efficiency and cost-effectiveness, especially in remote places where the primary grid is located far away. This research puts forth an optimum design of a PV-diesel hybrid energy system for analysis of a remote off-grid power system in St. John's, Newfoundland, Canada, using HOMER (Hybrid Optimization Model for Electric Renewables) software. The study involves connecting a diesel generator and a PV solar panel to the bus. To ensure uninterrupted load distribution, batteries are combined as storage. A technical and economic study of the hybrid energy system is conducted by simulation through HOMER. The levelized energy cost is expected to be lower under the proposed arrangement.

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)0.009
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0080.013
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.099
GPT teacher head0.357
Teacher spread0.258 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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