Data Mining for Electricity Price Classification and the Application to Demand-Side Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forecasting electricity prices plays a significant role in making optimal scheduling decisions in competitive electricity markets. Predominantly, price forecasting is performed from a “point forecasting” perspective, i.e., forecasting the exact values of future prices. However, in some applications, such as demand-side management, operation decisions are made based on certain price thresholds. It is, hence, desirable to obtain the “classes” of future prices, which can be cast as an electricity price classification problem. In this paper, we investigate the application and effectiveness of several data mining approaches for electricity market price classification. In addition, we propose a new data model for forming the initial data set for price classification. Simulation results for New York, Ontario, and Alberta electricity market prices are provided. Finally, the application of the generated numerical results to a demand-side management case study is demonstrated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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