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Record W7079204827

Estimation des composantes d'éclairement incliné par ciel clair à partir des luminances descendantes et montantes prenant en compte la réflectance du sol

2024· article· en· W7079204827 on OpenAlexaff

Bibliographic record

Venuetheses.fr (ABES) · 2024
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsImpact
Fundersnot available
KeywordsDownwellingIrradianceSolar irradianceUpwellingSkySolar energyPhotovoltaic system
DOInot available

Abstract

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Accurate prediction of downwelling and upwelling solar irradiances for various plane orientations is crucial for solar energy applications. Bifacial photovoltaic and solar tracking technologies significantly improve energy yields by capturing solar radiation on both the front and rear faces of photovoltaic modules, optimizing their orientations over time. These predictions are also vital in architecture and energy building, enhancing energy efficiency and ensuring visual and thermal comfort in buildings.For accurate predictions under all-sky conditions, it is essential to be able to provide first broadband direct, diffuse, and reflected solar irradiance components under clear skies, accounting for general ground reflectance. The objective of this PhD study is to explore the possibility of extending the capacity of the existing clear-sky irradiance model McClear, provided by the Copernicus Atmosphere Monitoring Services (CAMS). This model, which incorporates atmospheric parameters (ozone, water vapor, and aerosol properties) from CAMS, delivers fast and accurate downwelling global, diffuse horizontal irradiances, and direct normal irradiance under clear sky conditions but does not offer downwelling (diffuse) and upwelling (reflected) tilted irradiance.In this work, we propose a new model, McClear_Radiance, based on modeling downwelling and upwelling radiances, which are the sources for computing any irradiance component for arbitrary orientations. The McClear_Radiance model, extended from CAMS McClear, utilizes a LUT method using the radiative transfer model libRadtran. It relies on three key input parameters (diffuse fraction, aerosol type, and SZA) and it also incorporates ground reflectance through the Ross-Li modeling of the bidirectional reflectance distribution function (BRDF) for non-Lambertian surfaces, beyond albedo.Various validations and performance analyses have been conducted using two widely used empirical models as baselines to estimate tilted irradiances from direct and diffuse horizontal irradiances: the Perez Transposition model proposed by Perez et al. (1987) and a CIE-type radiance model by Perez et al. (1993a; 1993b). These studies were first performed in a simulated numerical environment using the RTM libRadtran as a reference for numerous clear-sky situations and various plane orientations, demonstrating significant improvement for the proposed McClear_Radiance model compared to the two baseline methods.“Real-world” validation of the McClear_Radiance model was finally conducted with high-quality concomitant global and diffuse horizontal, direct normal irradiance, and three global tilted irradiances (GTI, 20° South, 30° South, and 45° Southwest) from pyranometric sensors operated by DLR at the Plataforma Solar de Almería (PSA) in Southern Spain. Comparisons for the three orientations under clear sky conditions showed RMSE values of 20.1 W/m² (2.8%) and high correlation coefficients (≈ 0.998), consistent with the RMSE of clear-sky GHI from McClear (18.7 W/m², 2.9%), slightly better than the Perez Transposition model (23 W/m2, 3.2 %) and indistinguishable from the Perez Radiance models (20.1 W/m2, 2.8 %), considering measurement uncertainties.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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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