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Record W4280542479 · doi:10.18280/ejee.240201

A New Model to Predict the Global Solar Radiation GSR of Souk-Ahras City

2022· article· en· W4280542479 on OpenAlexvenueno aff
Boutora Tidjani, Nor Rebah, Dib Djalel

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2022
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsnot available
Fundersnot available
KeywordsRelative humidityEnvironmental scienceMeteorologyRadiationSunshine durationEmpirical modellingData setAtmospheric sciencesHumidityStatisticsGeographyMathematicsComputer sciencePhysicsSimulation

Abstract

fetched live from OpenAlex

The value of the global solar radiation GSR reaching the earth is very important because it is an essential variable for different applications. Unfortunately, solar radiation measurements are not available, most of the time, in developing countries because of the lack of measurement means. Moreover, these measurements are difficult to obtain under complicated weather conditions. Thus, solar radiation evaluation models are used. In this study, a new semi-empirical model for the estimation and prediction of the global solar radiation of Souk Ahras area in Algeria is proposed. The model developed is based on meteorological data such as daily hourly temperatures and average relative humidity required from several stations and databases, over a period of four years. The values of the regression coefficients a, b and c calculated are 0.0142, -10.6206 and 57.8367 respectively. To set the modal valid, we have applied it to 10 Algerian cities and we calculated the H/H0 ratio for each site from our model. They have been then compared with the values from the (CDER). We can conclude that our new model gives a good estimate of the average daily global solar radiation (H) for the studied regions (error between 2.49% and 8.93%) and can also be used elsewhere in areas with the same climatic conditions.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2022
Admission routes1
Has abstractyes

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