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Record W2181266271 · doi:10.19030/jss.v5i2.7577

Consumer Adoption Challenges To The Smart Grid

2012· article· en· W2181266271 on OpenAlexaffabout
Anu Gupta

Bibliographic record

VenueJournal of Service Science (JSS) · 2012
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSmart gridWork (physics)BusinessGridKey (lock)Computer securityMarketingComputer scienceRisk analysis (engineering)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The smart grid represents the next significant evolution of the power infrastructure with many enhancements and challenges. This paper provides a qualitative review of the key consumer adoption challenges of the smart grid. This literature review takes disparate pieces of work and identifies strategic research focal areas. Through existing research and media documents, it identifies the primary consumer adoption challenges as privacy, Radio Frequency (RF) safety, and power rate increases. It also provides a review of each adoption challenge. The review is applied to the Canadian perspective drawing from smart grid experiences across North America. The review demonstrates that each of the challenge areas has a negative impact on the adoption and support of the smart grid.This paper recommends further in-depth research be conducted in the following areas: testing each of the consumer adoption challengesprivacy, RF Safety, and rate increasesseparately with quantitative measures; testing the willingness to accept a power infrastructure with the security and stability level of a bank; and testing the impact that proactively educating the public would have on the smart grid adoption.

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)0.002
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 score0.829
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.235
Teacher spread0.209 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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