Power Load Forecasting Method Combining Informer Model and ACO Optimization Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This study proposes a power load forecasting method that combines the Informer model with the Ant Colony Optimization (ACO) algorithm. ACO optimizes the hyperparameters of the Informer model, significantly improving the model's accuracy and stability in power load forecasting. The Informer model utilizes its ProbSparse Attention technology to efficiently process long-term time series data and capture long-term dependencies in power load variations. ACO optimizes hyperparameter combinations through global search, avoiding the limitations of manual parameter tuning. Experimental results demonstrate that the proposed model outperforms traditional LSTM and TCN models across multiple evaluation metrics, demonstrating greater stability and prediction accuracy, particularly during peak load periods. This method provides effective technical support for smart grid scheduling and resource optimization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it