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Record W2795812752 · doi:10.1155/2018/8471715

Dynamic Modelling of a Solar Water Pumping System with Energy Storage

2018· article· en· W2795812752 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Solar Energy · 2018
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotovoltaic systemEnergy storageAutomotive engineeringTransformerWater pumpingEnvironmental scienceBattery (electricity)Solar energyComputer scienceElectrical engineeringProcess engineeringEngineeringMechanical engineeringVoltagePower (physics)

Abstract

fetched live from OpenAlex

This paper describes the dynamic modelling of a system used for extraction of groundwater for irrigation using an alternative source of energy. The system is designed based on data of an existing project in Lalmonirhat, Bangladesh. The system comprises a 38.4 kWp solar photovoltaic array, inverter, AC motor, and pump set, which can discharge a maximum of 1,930 m 3 of water per day. MATLAB simulation is performed with two types of energy storage system: (i) electric energy using a battery bank and (ii) stored water in a large water tank. A large battery bank and a transformer are needed in the former one, which turns out as a costly solution. The latter one requires a boost converter and a large water tank to store around 2,000 m 3 of water, which is also a costly solution. A combination of both systems yields an efficient and economical solution. The effectiveness of these three systems is compared with conventional diesel engine system.

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 categoriesnone
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 score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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