About The Conference
Bibliographic record
Abstract
Predicting the price of electricity is an important aspect in the operation and planning of power systems. However, predicting the price of electricity is a relatively challenging task as it faces very uncertain conditions. Hence, this study proposes a hybrid Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) for day-ahead electricity price forecast. The main contribution of this work is the multistage optimization approach of LSSVM-BFOA that can improve the forecasting accuracy and efficiency. This is achieved by optimizing the input features and parameters of LSSVM at the same time. The input features have been reduced by six optimization levels in order to avoid losing any significant input. At the same time, the average MAPE is observed and the second stage of optimization is carried out. These processes are performed until there is no improvement in MAPE is observed. This model is examined in the Ontario power market. The LSSVM-BFOA model developed showed higher prediction accuracy with less complex model structure than most existing models. The day ahead price forecast is beneficial for both power generators and consumers in bidding for electricity prices.
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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 | 0.000 |
| 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 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".