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Record W2608877326 · doi:10.1109/sustech.2016.7897140

Solar photovoltaic optimization for commercial flat rooftops in cold regions

2016· article· en· W2608877326 on OpenAlexafffundabout
Hadia Awad, Mustafa Gül, Chelsea Ritter, Preshit Verma, Yuan Chen, Khosru M Salim, Mohamed Al‐Hussein, Haitao Yu, Kyle Kasawski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsCapital Power (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotovoltaic systemInstallationCarbon footprintElectricity generationBuilding-integrated photovoltaicsRooftop photovoltaic power stationPhotovoltaic mounting systemCapital costFootprintAutomotive engineeringComputer sciencePayback periodReliability engineeringEngineeringPower (physics)Production (economics)Electrical engineeringMechanical engineeringGreenhouse gasMaximum power point trackingVoltage

Abstract

fetched live from OpenAlex

Solar photovoltaic systems are becoming increasingly popular as industries try to decrease their carbon footprint. This paper presents a generic optimization framework and examines a case study where a flat rooftop located in Edmonton, Alberta is investigated for the installation of a photovoltaic array. The objectives of this study are to maximize power generation while minimizing the system cost. This case study represents a proposed generic framework that fulfills this optimization problem. Solar power generation per month is forecasted using historical generation data. Panels can be installed at different tilt angles and varying inter-row spacing in order to achieve the optimal design. Designing an effective layout is important when installing solar panels, as the increased shade coverage or the wide variation from the normal angle of the sun can result in a loss of energy generation. The capital cost and payback period for the investment are also important factors when determining whether or not a photovoltaic system layout (inter-row spacing, tilt angle, etc.) is considered optimal. Analytic hierarchy process is used to weigh the decision factors and determine the optimal layout.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.249
Teacher spread0.226 · 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

Citations14
Published2016
Admission routes3
Has abstractyes

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