MétaCan
Menu
Back to cohort
Record W7162555085 · doi:10.24018/ejece.2025.9.6.19768

Modeling and Optimization of a Two-Household Off-Grid Solar PV System in Punjab, Pakistan using HOMER Pro

2025· article· W7162555085 on OpenAlexaff
Muhammad Saad Gondal, Mohammad Tariq Iqbal, Mohsin Jamil

Bibliographic record

VenueEuropean Journal of Electrical Engineering and Computer Science · 2025
Typearticle
Language
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsBrock UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPhotovoltaic systemCost of electricity by sourceRenewable energyBattery (electricity)ElectricityStand-alone power systemDiesel fuelGrid-connected photovoltaic power systemInverter

Abstract

fetched live from OpenAlex

This study presents the design and analysis of a stand-alone photovoltaic (PV) system with battery storage for two residential units located in Vanike Tarar, Punjab, Pakistan. The system was modelled and optimized using HOMER Pro based on local solar data, household load requirements, and component specifications. The configuration includes a 2.57 kW PV array composed of Jinko Solar 345 W modules, a 48 V battery bank consisting of four Trojan SAGM 12 205 batteries, and a Schneider Conext TX 3300 NA inverter rated at 0.995 kW. The total electrical load of about 7.45 kWh per day was supplied entirely by renewable energy, achieving a 100 percent renewable fraction. Annual excess electricity of 989 kWh and unmet load of 88.8 kWh were observed during simulation. The total net present cost of the system is estimated at Rs 644,846, resulting in a levelized cost of energy of Rs 18.96 per kWh. The results confirm that the proposed PV–battery system is technically feasible and economically suitable for remote households, providing clean and reliable electricity without dependence on diesel generation.

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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.213
Teacher spread0.201 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Electrical Engineering and Computer ScienceSame topicHybrid Renewable Energy SystemsFrench-language works237,207