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Record W2083197292 · doi:10.1109/iecon.2013.6699355

A predictive energy management strategy with pre-emptive load shedding for an islanded PV-battery microgrid

2013· article· en· W2083197292 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsMicrogridLoad SheddingPhotovoltaic systemBattery (electricity)Model predictive controlComputer scienceEnergy management systemEnergy managementReliability engineeringAutomotive engineeringEnergy storageState of chargeControl engineeringEnergy (signal processing)EngineeringControl (management)Electric power systemPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

A predictive energy management strategy for an islanded photovoltaic powered microgrid with battery storage is presented in this paper. The strategy incorporates generation forecasts built from on-line weather service models combined with local photovoltaic system specifications. An energy balance prediction based on the battery SOC, the forecasted generation, and the forecasted load is evaluated and used to identify upcoming outages and to initiate automated load shedding to extend the run time of the system. The technique is algorithmic in nature and well suited for implementation on a networked control platform. Simulated and accelerated experimental results are presented to validate the proposed technique.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.635

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Quick stats

Citations20
Published2013
Admission routes1
Has abstractyes

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