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Record W2014454218 · doi:10.1109/tsg.2015.2397003

Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators

2015· article· en· W2014454218 on OpenAlexaff
Yue Zhang, Marc Beaudin, Raouf Taheri, Hamidreza Zareipour, David Wood

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2015
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsUniversity of Calgary
FundersNational Oceanic and Atmospheric AdministrationDanmarks Tekniske Universitet
KeywordsPhotovoltaic systemSolar powerElectricity generationGrid parityElectricityScale (ratio)Stand-alone power systemEnvironmental scienceSolar energyMeteorologyPower (physics)Renewable energyPhotovoltaicsComputer scienceEngineeringDistributed generationElectrical engineeringGeographyPhysics

Abstract

fetched live from OpenAlex

Because of the rapid growth of small-scale solar electricity generation over the past few years, forecasting solar power output is becoming more important. However, changes in weather conditions cause solar power generation to be highly volatile. This paper analyses the challenges of solar power forecasting and then presents a similar day-based forecasting tool to do 24-h-ahead forecasting for small-scale solar power output forecasting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.248
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations190
Published2015
Admission routes1
Has abstractyes

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