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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".