Behind-the-Fence Generation Forecasting: A Batched Decomposition Framework
Bibliographic record
Abstract
In this paper, we carry out behind-the-fence (BTF) generation forecasting using a new decomposition framework called batched decomposition framework. Here, BTF is framed as a particular structuring of the behind-the-meter (BTM) problem, where power is produced at generation and industrial facilities for internal loads rather than being supplied directly to the grid. BTF forecast is important for power system operators as it aids planning and decision making. This study employs a novel decomposition framework that effectively manages the non-linearity of BTF data while preventing the information leakage issues commonly found in traditional decomposition approaches. To assess the effectiveness of the proposed batched decomposition framework, we tested it on forecasting 24 hours ahead BTF for two Canadian provinces, Alberta and Quebec. The proposed method demonstrates high forecasting accuracy, comparable to the traditional decomposition method, while also avoiding information leakage and ensuring the practicability of the solution. Additionally, the results of the proposed method is benchmarked against various state of the art models using various error metrics. The batched decomposition method was shown to outperform the benchmarks for both test cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".