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Record W3188922109 · doi:10.1109/mcom.001.2001140

Intelligent Photovoltaic Power Forecasting Methods for a Sustainable Electricity Market of Smart Micro-Grid

2021· article· en· W3188922109 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

VenueIEEE Communications Magazine · 2021
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSoftware deploymentStandardizationElectricitySmart gridComputer scienceElectricity marketSustainabilityGridEnvironmental economicsProfit (economics)Photovoltaic systemElectricity generationTelecommunicationsOperations researchPower (physics)Electrical engineeringEconomicsEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

Smart micro-grid (SMG) is a growing segment of the modern power grid. Besides the benefits brought in terms of improving power reliability, power quality, security, and sustainability, and promoting competitiveness in a new deregulated electricity market, SMG is still in the early commer <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sup> ial stage. This article introduces the SMG con-cept-based forecasting mechanisms. It highlights the role of the power production and profit forecasting methods to drive the SMG deployment with the liberalized electricity market concept. Finally, some open issues in SMG deployment such as standardization and regulations are presented as key drivers for SMG success.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.488
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.297
Teacher spread0.262 · 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