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Record W2765990772 · doi:10.21608/ejmtc.2017.743.1034

Sizing of a Photovoltaic System for a House in Qassim, Saudi Arabia

2017· article· en· W2765990772 on OpenAlexaff
Saif Graduate, M. Tariq Iqbal

Bibliographic record

VenueJournal of Engineering Science and Military Technologies /Journal of Engineering Science and Military Technologies · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPhotovoltaic systemSizingElectricityArchitectural engineeringBattery (electricity)Solar energyConsumption (sociology)Renewable energyAutomotive engineeringEnvironmental scienceComputer scienceEngineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

Consumption of electricity in the Middle East is quite high due to the cooling needs in each home. However, Saudi Arabia has high solar energy resources that could be used to meet all home energy needs. In this project, a solar energy system is designed using BEopt and Homer softwares. BEopt was used to build a thermal model for an actual house in Qassim, Saudi Arabia, to stimulate the hourly kilowatt electricity consumption. Mathematical equations have been used to calculate the necessary photovoltaic and battery size. The collected data and BEopt results are used by Homer software to design various options for a photovoltaic system (PV). Results indicate that a 18.85 kW PV system, 52 batteries 200Ahr (Ah) each and a 10kW inverter can meet all house energy needs.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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