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Record W2045976629 · doi:10.1109/wcsp.2011.6096965

Energy efficient communication networks design for demand response in smart grid

2011· article· en· W2045976629 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 institutionsUniversity of Victoria
Fundersnot available
KeywordsSmart gridComputer scienceDemand responseComputer networkWirelessDistributed computingTelecommunications networkNetwork packetEnergy consumptionCommunications systemPower controlPacket lossPower (physics)TelecommunicationsEngineeringElectricityElectrical engineering

Abstract

fetched live from OpenAlex

The convergence of electrical power control systems and communication techniques enables the intelligence over current and future power grid system which evolves to the smart grid. Demand response (DR) is considered as a killer application for so-called smart grid. Real-time DR control relies on efficient and reliable communication services. In this paper, the impact of packet losses during communication on DR control has been investigated, using the control strategy in [1]. Then, an analytical model for quantifying the performance of packet loss and energy consumption for transmission in a clustering-based multi-hop wireless communication network has been established. Finally, how to improve the design of wireless communication networks is proposed to satisfy the DR control requirements and to minimize the energy consumption for communications.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.203
Teacher spread0.176 · 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

Citations39
Published2011
Admission routes1
Has abstractyes

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