Prediction of electricity prices for non‐regulated markets based on a power transformed mean reverting process
Bibliographic record
Abstract
Abstract The electricity price time series for non‐regulated markets presents two basic properties: (a) a mean reversion trend around a constant or deterministic function and (b) a price process with high volatility and marginal, conditional, and asymptotic distributions skewed to the right. We propose a microeconomic‐based model for the dynamics of electricity prices that provides a satisfactory explanation of the stylized features observed in non‐regulated electricity markets. The suggested model is based on a power transformation of the Ornstein–Uhlenbeck process, which accounts for the unobserved demand process. This means that the model requires only the spot price of the electricity price to be available. Parameter estimates were obtained using the maximum likelihood method. This approach was implemented for data from the Alberta electricity market in Canada, with satisfactory results in terms of residual analysis and forecasting. The success rate of predicting the price jump sign (upward and downward) was approximately . Moreover, the statistical properties for the maximum likelihood parameter estimators are shown via a Monte Carlo simulation of the electricity price time series.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".