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Record W2006627781 · doi:10.1109/irec.2014.6826905

Very short term forecasting of the Global Horizontal Irradiance through Helioclim maps

2014· preprint· en· W2006627781 on OpenAlexaff
Romain Dambreville, Philippe Blanc, Jocelyn Chanussot, Didier Boldo, Stéphanie Dubost

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsImpact
Fundersnot available
KeywordsIrradianceGeostationary orbitComputer scienceSolar irradianceGridTerm (time)Data setMeteorologyRemote sensingBlock (permutation group theory)Set (abstract data type)SatelliteEnvironmental scienceData miningArtificial intelligenceGeographyMathematicsGeodesyEngineering

Abstract

fetched live from OpenAlex

To handle the increasing penetration rate of intermittent energies such as photo-voltaic power plants, grid managers require accurate forecasts of the primary resources. In this paper, we propose a new method to forecast the Global Horizontal Irradiance (GHI) up to 1 hour using HelioClim-3 maps as only inputs. HelioClim-3 maps are Solar Surface Irradiance (SSI) maps derived from geostationary satellite images through the Heliosat-2 method. We use a block matching algorithm to retrieve the motion vector field, allowing us to anticipate the future on site GHI values. The results were then corrected following a statistical post-processing step tuned on a training data set. The model has been tested on a one year data set acquired on the SIRTA site, Polytechnique campus, France. Besides the very promising performances of the proposed method compared to the persistence model, we extracted additional forecast information from the SSI maps like the minimum and maximum expected values, which enlighten the model's potential and limitations.

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: none
Teacher disagreement score0.726
Threshold uncertainty score0.837

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 science0.0020.002
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 head0.039
GPT teacher head0.266
Teacher spread0.226 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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