A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting
Bibliographic record
Abstract
Price prediction is important to market members in deregulated electricity environment to provide a better maintenance scheduling, developing investment, medium term planning, as well as decision-making.However, forecasting electricity price is a challenging task due to the volatility of price series with unexpected price spikes at any point of series.In addition, medium term forecast is more challenging than short-term price forecast, due to limited Abstract: Predicting electricity price has now become an important task for planning and maintenance of power system.In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead.It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario.However, researches on medium term price forecast have also exhibit low forecast accuracy.This is due to the limited historical data for training and testing purposes.Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features.So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction.The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide.Monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features.The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than the existing models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".