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Record W2742717540 · doi:10.1109/tsg.2017.2737946

AMI-Based Energy Management for Islanded AC/DC Microgrids Utilizing Energy Conservation and Optimization

2017· article· en· W2742717540 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

VenueIEEE Transactions on Smart Grid · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrogridEnergy managementMetering modeComputer scienceEnergy conservationEngineeringEnergy management systemEnergy (signal processing)Reliability engineeringControl engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents a new approach for quasi real-time and adaptive energy management for islanded ac/dc microgrids using advanced metering infrastructure (AMI) data. This paper presents a sophisticated multi-objective algorithm by employing AMI-based energy conservation and optimization techniques, tailored for different operational scenarios. This paper argues that optimal operation can be achieved by defining new concepts, such as operation value factor that can inherently reduce system operational costs. Moreover, the results of this paper demonstrate how utilizing accurate load models can improve ac/dc microgrid efficiency. To test the performance and the applicability of the proposed solution, a modified 33-node islanded ac/dc microgrid is used as a case study in the presence of various types of ac/dc generating units and loads.

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 score0.955
Threshold uncertainty score0.971

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 studies0.0010.000
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 head0.206
Teacher spread0.195 · 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