Multivariate Power Load Forecasting Model Considering Meteorological Feature-Load Dynamic Forward Lag and Turning Points
Bibliographic record
Abstract
Due to building thermal inertia and delayed user behavioral responses, power load often lags behind meteorological changes, particularly drops in temperature and humidity. Existing models tend to overlook this dynamic lag relationship. To address these challenges, this study proposes a power load forecasting model that integrates three key mechanisms: a dynamic forward lag mechanism, a turning point attention mechanism, and a temporal and channel hybrid mechanism. These components collectively enable the forecasting model to adaptively align meteorological lags, focus on abrupt load transitions, and capture both global temporal dependencies and local feature interactions, thereby enhancing its ability to model complex load behaviors and improve prediction accuracy. Seven experiments were conducted using load data from Singapore and Calgary, Canada, to validate the effectiveness of the proposed model. Numerical results show that the proposed method achieves higher forecasting accuracy compared to existing load forecasting approaches.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".