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Impact study of collaborative implementation models on total cost of ownership of integrated fiber-wireless smart grid communications infrastructures

2013· article· en· W2075660456 on OpenAlexaff
Ramzi Charni, Martin Maier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsTotal cost of ownershipSmart gridComputer scienceTelecommunicationsRenewable energyGridService providerBroadbandKey (lock)Service (business)BusinessComputer securityEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The majority of previous studies give good insight in the overall costs of various communications network architectures but most consider only the vertical integration model. However, it is necessary to take a closer look at the possibilities and gains through collaboration. In this work, we develop a flexible, generic yet comprehensive total cost of ownership (TCO) framework, which calculates the overall costs related to the rollout of smart grid communications networks for different scenarios. Further, we present our novel collaborative implementation model for a shared infrastructure for both broadband access and smart grid communications. In addition, buildings currently shift from a product to a service (i.e., renewable power supply). Thus, our idea is that housing companies will collaborate by offering the positive renewable energy to communications network providers. We study the impact of this model on TCO and compare it with tradionnal vertical integration model. The sensitivity analysis for the key cost parameters is conducted. We also analyze the risk related to renewable energy intermittency.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.464

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 head0.028
GPT teacher head0.313
Teacher spread0.285 · 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 teacher head, 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

Citations6
Published2013
Admission routes1
Has abstractyes

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