Modelling Spot Prices in Deregulated Wholesale Electricity Markets: A Selected Empirical Review
Why this work is in the frame
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Bibliographic record
Abstract
The restructuring and deregulation of global electricity markets has brought about fundamental changes in the behaviour of wholesale spot prices. In turn, this has fostered a small but increasing volume of literature aimed at modelling and providing best-practice forecasts of electricity prices and price volatility, often employing very high-frequency data. The purpose of this paper is to review the various time-series regression modelling approaches as they apply to competitive electricity markets throughout the world. Apart from discussing the strengths and weaknesses of the different approaches and their key findings, the paper also examines the steps faced by researchers as they move through the modelling process. Accordingly, the article provides guidance to those conducting empirical research on electricity prices and serves as an aid for policymakers, managers and practitioners interpreting electricity pricing research outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 it