Short-Term Demand Prediction Using an Ensemble of Linearly-Constrained Estimators
Bibliographic record
Abstract
The benefits of forecasting power demand can bring increased stability to any power grid. Between optimizing the production and control of grid resources and interacting with energy markets, there is a strong motivation for generation, transmission, and distribution grid stakeholders to obtain accurate power demand prediction, which requires more sophisticated prediction methods. We introduce an ensemble of linear predictive nodes called the Ensemble Prediction Network (EPN), which optimizes demand prediction motivated by various microgrid considerations. EPN outputs a nonlinear combination of the individual predictions whose mixing weights are optimized in the least-squares sense. Using a large number of publicly available datasets, we show that on-the-whole, EPN provides substantial improvement relative to each individual predictor. Furthermore, we compare our method with a Long Short-Term Memory (LSTM) neural network and a multi-layer perceptron, and demonstrate the advantages of the proposed method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".