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Record W2158930128

Probabilistic Model and Solution Algorithm for the Electricity Retailers in the Italian Market

2011· article· en· W2158930128 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAlgorithmic operations research · 2011
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBiddingSimulated annealingElectricity marketComputer scienceProbabilistic logicElectricityMathematical optimizationPortfolioAlgorithmEconomicsMathematicsArtificial intelligenceMicroeconomicsFinanceEngineering
DOInot available

Abstract

fetched live from OpenAlex

The paper considers the problem of maximizing the profits of aretailer operating in the Italian electricity market. The problem consists in selecting the contracts portfolio and i n defining the bidding strategy in the wholesales market whil e respecting the technical and regulatory constraints. A nov el solution method based on a enhanced discovery of the searc h domain in the simulated annealing technique has been develo ped for its solution and a set of realistic test problems have been generated for its validation. The experimental result s show that our method outperforms the standard simulated annealing by an improvement gap of 20,48% in average.

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)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.000
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 head0.069
GPT teacher head0.298
Teacher spread0.228 · 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