ACCEPTED TO IEEE TRANSACTIONS ON POWER SYSTEMS 1 Application of Public-Domain Market Information to Forecast Ontario’s Wholesale Electricity Prices
Bibliographic record
Abstract
Abstract — This paper evaluates the usefulness of publicly available electricity market information in forecasting the hourly Ontario energy price (HOEP). In order to do so, relevant data from Ontario and its neighboring electricity markets, namely New York, New England, and PJM electricity markets are investigated, and a final set of explanatory variable candidates that are available before real-time are selected. Multivariate transfer function and dynamic regression models are employed to relate HOEP behavior to the selected explanatory variable candidates. Univariate ARIMA models are also developed for the HOEP. The HOEP models are developed on the basis of two forecasting horizons, i.e. 3 hours and 24 hours, and forecasting performance of the multivariate models is compared with that of the univariate models. The outcomes show that the market information publicly available before real-time can be used to improve HOEP forecast accuracy to some extent; however, unusually high or low prices remain unpredictable, and hence, the available data cannot lead to significantly more accurate forecasts. Nevertheless, the generated forecasts in this work are significantly more accurate than currently available HOEP forecasts. To analyze the relatively low accuracy of the HOEP forecasts, comparisons with respect to ARIMA models developed for locational marginal prices (LMPs) of Ontario’s three neighboring markets and price volatility analyses are presented. Index Terms — Price forecasting, electricity markets, time series models, volatility analysis. I.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.066 | 0.011 |
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".