Prediction of coincident peak days in electricity system: a case study for classification on imbalanced data
Bibliographic record
Abstract
To guarantee sufficient electricity supply for its highest demands, many regional organizations surcharge their customers during coincident peaks (CPs), a time of highest demand across the system or region of interest. Therefore, the accurate prediction of these coincident peaks would be helpful not only for companies to ensure sufficient generation is available, but also for customers who may try to avoid electricity consumption and consequent additional cost. This dissertation focuses on the prediction of the top five coincident peak days (5CPs) in a year. We use classification models to solve this imbalanced prediction problem (around 1.4% for positive cases) by classifying the next day as 5CP days or non-5CP days. We analyze six sets of actual historical data from different regions of Canada and the United States. We explore the effect of forecast accuracy on 5CP days prediction through four cases: I - knowing tomorrow’s power demand and weather condition exactly (an oracle), II & III - knowing some information about tomorrow (an oracle + increasing noise), and IV - no knowledge of future. We propose a three-phase model to predict 5CP days: first, clustering is applied to filter some negative cases, second, an all convolutional neural network that estimates the probability of being a 5CP day for the remaining cases is learned, and third, an adaptive method is used determines thresholds. This three-phase model exhibits promising performances with the highest mean recall of 1.00, mean precision of 0.56, and mean F1 score of 0.72. Finally, we explore the use of a few-short learning framework for this problem. A triplet network is implemented for the 2-way-5-shot classifications. The prediction results have the highest mean recall of 1.00, mean precision of 0.67, and mean F1 score of 0.79.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".