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Photovoltaic Shading and Performance Evaluation in an Offshore Hybrid Wind-Solar Platform

2025· article· W7117471914 on OpenAlexaboutno aff
Ciprian Popa, Nicolae-Silviu Popa, Florențiu Deliu, Mihai-Octavian Popescu

Bibliographic record

Venuenot available
Typearticle
Language
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemSubmarine pipelineShadingPort (circuit theory)TurbineSolar energyFossil fuelElectricity

Abstract

fetched live from OpenAlex

This study proposes and evaluates a hybrid photovoltaic-wind platform designed as an autonomous semisubmersible offshore charging station for vessels at anchorage. The research emphasizes the geometric configuration and shading analysis of photovoltaic modules, both in the horizontal and vertical planes, to determine optimal spacing and maximize energy capture. A case study was conducted for the outer anchorage area of the Port of Constanta <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\left(44^{\circ} 1^{\prime} \mathrm{N}, 28^{\circ} 7^{\prime} \mathrm{E}\right)$</tex>, where Canadian Solar CS7N-660MS modules were considered. The results showed that a total of 72 modules (48 installed on the floating platform and 24 on the turbine tower) can be integrated over an active surface of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$216 ~\mathrm{m}^{2}$</tex>, corresponding to an installed capacity of approximately 47 kW. Based on irradiation data from the Global Solar Atlas and standardized performance models, the annual energy output was estimated at nearly 56 MWh. These findings validate the technical feasibility of the proposed configuration and demonstrate its potential to reduce fossil fuel consumption and pollutant emissions from maritime activities.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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