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Record W2111659384 · doi:10.1109/ccece.1999.804865

Wavelet neural network based short term load forecasting of electric power system commercial load

2003· article· en· W2111659384 on OpenAlex
Anant Oonsivilai, M.E. El-Hawary

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsDalhousie University
Fundersnot available
KeywordsArtificial neural networkMorlet waveletWaveletElectrical loadElectric power systemComputer scienceWavelet transformElectric powerPower (physics)Term (time)Artificial intelligenceEngineeringDiscrete wavelet transformElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

In an electric power system, the system load consists of domestic, commercial, industrial and municipal load sectors. This paper presents an approach for predicting electric power system commercial load using a wavelet neural network. Morlet and Mexican hat wavelets are used to generate the transfer functions of hidden layer nodes of the neural network. A wavelet neural network is trained for a particular power system load. Results show that wavelet neural networks may outperform traditional architectures in approximation and forecasting problems related to electric power system.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.201
Teacher spread0.185 · 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

Quick stats

Citations54
Published2003
Admission routes1
Has abstractyes

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