A Deployment-Oriented Extreme Learning Machine for Electricity Price Forecasting
Bibliographic record
Abstract
In electricity price forecasting research, deployment is often an afterthought. The literature has largely emphasized computationally expensive deep learning architectures, creating a disconnect between the needs of smart grid operators and resource-intensive approaches. In addition, prior studies often depend on non-public datasets or suffer from data leakage, which limits reproducibility and claims of real-time applicability. We address this gap by demonstrating that lightweight models can exceed both operational baselines and a long short-term memory (LSTM) alternative, while enabling practical edge deployment. Using the public archives of the Independent Electricity System Operator (IESO), we develop an Extreme Learning Machine (ELM) for multi-horizon Hourly Ontario Energy Price (HOEP) forecasting that predicts$1-3$hours ahead simultaneously. Our model outperforms IESO's predispatch forecasts by an average of 21% across the three horizons and the LSTM baseline by 2.4%. Additionally, our ELM trains$10 \times$faster than the LSTM baseline and achieves sub-5 ms inference on low-cost edge hardware (Raspberry Pi 4). These results demonstrate that deployment considerations need not compromise forecasting accuracy, enabling adoption in edge device environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".