Price Forecasting for Day Ahead Energy Markets: A Difference Boosting Approach
Bibliographic record
Abstract
As the energy sector is being restructured, the way electrical power is traded is changing across the globe and energy trading are becoming the focal points of the power grids. In developed energy markets, energy trading is pivotal and plays a crucial role in the modern economy, impacting both producers and consumers. Forecasts for the day ahead energy market prices play a critical role for the market participants, who formulate their bidding decision based on the forecasts. Reliable and accurate forecasts translate to better bidding decision and impacts the overall market efficiency. While machine learning based forecasting solutions have resulted in better market price forecasts, these models have an underlying assumption that the system under consideration is stationary, which does not hold for energy market prices since both the prices and its covariates have long term varying trend making them non-stationary systems. In this work, we propose a difference learning based machine learning approach for forecasting day ahead energy market price. Our approach uses gradient boosted trees to learn the deviations in prices from its corresponding covariates. To evaluate the performance of the proposed technique, two distinct markets are selected-the Midcontinent ISO electricity markets, and the Danish market from the DK2 region. We report improved forecasting accuracy with the proposed technique while compared with state of the art forecasting techniques.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".