Price forecasting using computational intelligence techniques: A comparative analysis
Bibliographic record
Abstract
Deregulation of Power market has initiated a multitude of reforms in the electricity sector aiming to make it more efficient, transparent and friendly to both the consumers and the suppliers. Accurate forecasting of the future electricity prices has become the most important management goal since it forms the basis of maximizing profits for the market participants. Electricity price forecasting however is a complex task due to non-linearity, non-stationarity and volatility of the price signal. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. An optimum selection amongst a large number of various input combinations and parameters is a real challenge for any modelers in using SVMs. This study applies SVM to predicting the hourly market prices of Ontario market. Optimal parameters of SVM are determined using computational intelligence techniques such as Genetic algorithm, Particle Swarm Optimization and Quantum inspired Particles Swarm Optimization (QPSO). A detailed analysis of these techniques has been performed to evaluate their robustness and ability to reach global solution in different scenarios and using different models.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".