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Record W4388102959 · doi:10.18280/ijsdp.181021

Floating Solar: A Review on the Comparison of Efficiency, Issues, and Projections with Ground-Mounted Solar Photovoltaics

2023· review· en· W4388102959 on OpenAlexvenueno aff
Dipen Paul, D. Devaprakasam, Suyash Patil, A. P. Agrawal

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typereview
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaicsEngineering physicsPhotovoltaic systemEnvironmental scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Electricity generation is said to be a significant contributor to climate change.Now as the power demand is increasing daily, certain green innovations and technologies are emerging to cater to the energy demand.One such technology is Floating Solar Photovoltaic (PV) systems which helps to overcome conventional ground mounted solar systems.The purpose of the paper is to compare the Floating PV systems and ground mounted PV systems in terms of efficiency and energy projections.A secondary research was undertaken through global research databases such as Scopus and Web of Science to study the current body of literature.This literature review encompassed recent global cases, industry-related reports and policies/frameworks to analyze electricity generation, system efficiency and suggestions from secondary research on improving the prospects of Floating PV systems.The key finding emerging from the review was that Floating PV systems can resolve issues related to land availability and lower environmental impact.Based on the review it was also inferred that there is huge market potential which is untapped in comparison with ground-mounted PV.The study on Floating PV provides crucial insights for stakeholders, influencing decision-making, strategy development, shaping the future of renewable energy adoption and sustainable development.

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 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 score0.898
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.067
GPT teacher head0.354
Teacher spread0.287 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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