A novel intelligent strategy for probabilistic electricity price forecasting: Wavelet neural network based modified dolphin optimization algorithm
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.
Bibliographic record
Abstract
To simplify decision making of market participants, a careful and reliable electricity market price forecasting method is indispensable. Nevertheless, due to the Instability in market clearing prices (MCPs), it is rather tough to forecast MCPs accurately. Using probabilistic forecasting is a new so lution to overcome the low accuracy of forecast. Transformation from traditional point forecasts to probabilistic interval forecasts is too important to model the uncertainties of forecasts. Thus the decision making activities of market participants are supported against uncertainties and risks effectively. In this paper a hybrid approach to achieve prediction intervals (PIs) of MCPs is proposed that modified dolphin echolocation optimization algorithm (MDEOA) is applied to estimate point forecasts, model uncertainties, and noise variance. This proposed electricity price probabilistic forecasting method is evaluated by a generalized and comprehensive framework. To test the proposed hybrid method, real price data from Ontario, New England, and, Australian electricity markets are used and effectiveness of the method is validated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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