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Record W2904502618

CIGRE' Task Force C6.04.02: Developing benchmark models for integrating distributed energy resources

2005· article· en· W2904502618 on OpenAlexaboutno aff
Stefano Barsali, Kai Strunz, Zbigniew A. Styczynski

Bibliographic record

VenueCINECA IRIS Institutial research information system (University of Pisa) · 2005
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmark (surveying)Task (project management)Task forceComputer scienceEnergy (signal processing)Energy resourcesEnvironmental scienceEngineeringSystems engineeringPhysicsGeographyPolitical scienceCartography
DOInot available

Abstract

fetched live from OpenAlex

Task Force C6.04.01 has been formed in order to elaborate recommendations for technical rules to be integrated in the national distribution grid codes and for the development of international standards, in order to keep a safe operation of power systems with reasonable and economically acceptable requirements for dispersed generation of various types. Emphasis has been placed on criteria that put unnecessary restrictions on DG penetration. The Task Force has produced a brochure [1] with the following contents:\n\n1.\tReview of the current connection criteria and protection practices applied in various countries for DGs, with special emphasis on the case of wind generators/wind farm integration.\n\n2.\tReview of existing international standards.\n\n3.\tDescription of simplified methods applied to DG connection in various countries\n\n4.\tIdentification of methods to meet the new requirements.\n\n5.\tFormulation of recommendations\n\n\n\nThe Task Force comprises 21 members from 15 countries contributing the experience from France, Spain, Italy, Germany, Austria, USA, Canada, Japan, Belgium, Greece, Portugal, Norway, the Netherlands, Croatia and Saudi Arabia.

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)0.001
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 score0.968
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.033
GPT teacher head0.241
Teacher spread0.208 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

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