Improved short-term electricity load forecasting using extreme learning machines
Why this work is in the frame
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Bibliographic record
Abstract
Short term forecasting is an essential tool in energy companies to take the decisions about power generation, transmission and day-to-day utility operations. A number of techniques are used in the field of Short Term Load Forecasting (STLF), like statistical and artificial neural network technique. This paper proposes an extreme learning machine based STLF technique that considers relative difference in percentage of load(RDL) at different intervals as one of the main characteristics of the system load. Here for analysis, historical data are taken from Independent Electricity System Operator(IESO) for Ontario province. Forecasting results obtained by this new approach have been presented and compared with the benchmark neural network based model like NN-GA, NN-PSO and NN-ABC, which confirms its applicability in forecasting domain.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it