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Genetic optimization of a fuzzy control system for energy flow management in micro-grids

2013· article· en· W2008800859 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
TopicSmart Grid Energy Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrogridFuzzy logicComputer scienceEnergy managementGenetic algorithmFuzzy control systemEnergy storageController (irrigation)Energy management systemControl engineeringGridSmart gridEnergy (signal processing)Power (physics)Control (management)EngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we present an interesting application of Computational Intelligence techniques for the power demand side and flow management optimization in a microgrid. In particular, we used a Fuzzy Logic Controller (FLC) for Time-of use Cost Management program in the microgrid. FLC can either sell and buy energy from outside the microgrid making use of an aggregate of energy storage capacity realized with lithium ion batteries. According to the hybrid Fuzzy-GA paradigm, the Fuzzy Logic Controller that operates decision making on energy flows is optimized by a Genetic Algorithm. The experimental results show that the proposed control system can manage effectively the energy trade with the main grid on the basis of real time prices.

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: Methods · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.521

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 head0.003
GPT teacher head0.156
Teacher spread0.153 · 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

Citations41
Published2013
Admission routes1
Has abstractyes

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