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Record W2522092224 · doi:10.1109/eeeic.2016.7555531

Long- and short-term electric load forecasting on quarter-hour data: A 3-torus approach

2016· article· en· W2522092224 on OpenAlexaboutno aff
Alice Guerini, Giuseppe De Nicolao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)Quarter (Canadian coin)EconometricsComputer scienceDetrended fluctuation analysisStatisticsMathematicsScalingGeography

Abstract

fetched live from OpenAlex

Long- and short-term forecasting of the Italian electric power load is investigated on a quarter-hour time scale. The detrended and log-transformed data are modelled as the sum of a seasonal term (the potential) and intervention terms accounting for holidays. Yearly, weekly and daily periodicities are effectively captured by modelling the potential as a function defined on a 3-torus. The model yields long-term whole-year predictions, that, with a suitable correction, provide also short-term whole day predictions. When tested on Italian 2013 load data, the new one-day-ahead forecaster performs definitely better than two state-of-art forecasters currently used by the Italian TSO.

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)0.000
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 score0.957
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.227
Teacher spread0.189 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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