Regularized Probabilistic Forecasting of Electricity Wholesale Price and Demand
Bibliographic record
Abstract
Electricity price forecasting is a fundamental step for power producers in competitive electricity markets, although it is a challenging task. Participation of renewable power plants in the electricity supply chain has increased uncertainty of electricity supply, demand, and price. Probabilistic forecasting approaches are the proper tools to take into account this uncertainty. In this paper, we use the double exponential smoothing (DES) as well as triple exponential smoothing (TES) methods to forecast electricity price volatility. Regularized forecasts for volatility have been studied using the elastic net regularization method. Sample sign correlation of standardized electricity prices (standardized by volatility forecasts) is used to identify the conditional distribution of electricity price time series. Validation of the regularized volatility forecasts is demonstrated using the publicly available hourly electricity price data of Ontario. Our data analysis results show that TES forecasts of volatility outperforms DES. In addition, elastic net regularization decreases the mean square error of TES volatility forecasting from 72.95 to 59.16. The regularized probabilistic forecast of electricity demand is used to implement a decision analysis approach and to model the scheduling of power generation units in the electricity market.
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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".