MétaCan
Menu
Back to cohort

Development and Improving Energy Management System in Smart Grid through Integration with Renewable Energy System with AI and Internet of Things

2023· article· en· W4378980144 on OpenAlexaff
Reena Bhavya, P Manjuladevi., E. Sri Santhoshini, M. Ramya, R. Dhanapal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsSmart gridRenewable energyThe InternetComputer scienceEnergy managementElectricityEnergy management systemGridTelecommunicationsEnergy (signal processing)Electrical engineeringEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

An innovative development in the energy substructure is defined as smart grid. The process of maintaining the stability between the supply side and the demand side is denoted as energy management system in smart grid. Further various cutting end solutions are obtained through the internet of things which helps in the overall conversion of conventional power grid into a smart grid network. This leads to rise in the energy internet which is formulated through the interrelationship between electricity producers cum consumers with renewable energy sources. This includes real time implementation with bidirectional flow of power through communication network and considering cost parameter. Thus the various challenges in the electricity production and distribution are done smartly through incorporating artificial intelligence and internet of things.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.166
Teacher spread0.161 · 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
GenreMethods

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicSmart Grid Security and ResilienceFrench-language works237,207