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Record W4200496613 · doi:10.18280/ijsdp.160811

Solar Energy Potential Evaluation. Case of Study: M'Sila, an Algerian Province

2021· article· en· W4200496613 on OpenAlexvenueno aff
Younes Kherbiche, Nabila Ihaddadène, Razika Ihaddadène, Feres Hadji, Jed Mohamed, A. Beghidja

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemEnvironmental scienceSolar energyParabolic troughMeteorologyConcentrated solar powerIrradiationAtmospheric sciencesElectricityEngineeringGeographyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Solar energy is the most important green energy source that may be used to address the problems caused by conventional energy sources. The research in this paper aims to evaluate the solar energy potential of M'Sila, an Algerian internal region, and to determine which technology, among those currently available on the market, is adequate for harnessing that potential for electricity production (PV and CSP). This assessment was based on data (daily temperature and daily solar irradiation) collected at M'Sila weather station for one year and from the NASA site for 34 years from 1984 to 2017. These data were treated with excel 2016 software to determine the total horizontal, diffuse and direct normal solar irradiation. During the study period, the total solar irradiation falling on a horizontal surface in M'Sila (GHI) was estimated to be 1.79 MWh/m2/year. The diffuse solar irradiation incident on a horizontal surface (DHI) and direct normal irradiation, respectively, were estimated to be 594.88 kWh/m2/year, 2117.93 kWh/m2/year. M'Sila is a favourable land for the installation of concentrating solar power plants and photovoltaic panels due to its significant potential energy. The parabolic trough system is recommended. While polycrystalline PV modules should be promoted and installed in M'Sila.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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 designQualitative
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

Citations10
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Sustainable Development and PlanningSame topicSolar Radiation and PhotovoltaicsFrench-language works237,207