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Modeling Advanced Metering Infrastructure with Bidirectional Traffic in Smart Grids

2024· article· en· W4402157456 on OpenAlexaff
Haitham Abu Ghazaleh, Attahiru Sule Alfa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMetering modeComputer scienceSmart gridComputer networkEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Traditional power grids have been predominantly one-way delivery systems. Recently, a two-way power delivery and communication system has been proposed to be incorporated into the current power grid system, thereby establishing a Smart Grid, to alleviate the challenges associated with one-way systems. The advanced system includes a large number of sensors deployed at multiple locations and consumer sites (e.g. Smart Meters) for monitoring usage and faults. The two-way communication infrastructure provides the utility control centers with the means to collect data on power usage from the smart meters, along with the ability to communicate back operation-critical information and control actions to their client's smart meters, thus establishing an infrastructure for both monitoring and controlling different services on the grid. In this paper, we suggest a framework of the network model for analyzing the performance of bidirectional data communication in the advanced metering infrastructure, along with our future directions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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