MétaCan
Menu
Back to cohort
Record W4407919452 · doi:10.1016/j.procs.2025.01.334

Single-Axis Solar Tracking Systems: A Comprehensive Design and Performance Study

2025· article· en· W4407919452 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsNSCAD University
Fundersnot available
KeywordsComputer scienceTracking (education)Tracking systemSimulationArtificial intelligenceKalman filter

Abstract

fetched live from OpenAlex

Abundant solar resources and increasing electricity demand make solar energy a promising renewable energy source in Sub-Saharan Africa. However, conventional stationary Photovoltaic (PV) systems face challenges in efficiently capturing solar irradiation. To address this limitation, the implementation of solar tracking systems becomes essential, as they optimize the energy output of PV systems by dynamically adjusting the orientation of the panels to align with the sun’s rays. This study presents a comprehensive design and performance evaluation of single-axis solar tracking systems in Delta State, Nigeria. The investigation focused on the energy provision efficiency of these systems, revealing that the single-axis tracker reached peak performance at year-end, providing 9.333 kWh of available solar energy and 9.296 kWh of user-available energy. Over the year, the system delivered a total of 100.625 kWh of available solar energy and 96.483 kWh of user-available energy. These findings highlight the significant role of solar tracking systems in maximizing energy harvest and underscore their potential for improving energy sustainability in Sub-Saharan Africa.

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.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.037
GPT teacher head0.257
Teacher spread0.221 · 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