Q-Learning-Based Approach for Mitigating Peak Shaving Impact on Total Demand Load Forecasting
Bibliographic record
Abstract
Effective management of peak energy demand is crucial for ensuring grid stability and minimizing operational costs in modern utility systems. However, peak shaving strategies, commonly employed by utilities to mitigate peak demand, can inadvertently introduce distortions in load patterns, thereby compromising the accuracy of load forecasting models. In this paper, we address this challenge by proposing a novel approach leveraging reinforcement learning techniques, specifically Q-learning, to mitigate the adverse effects of peak shaving on load forecasting accuracy. Our approach involves integrating Qlearning at a higher level within the forecasting framework to dynamically adjust forecasting values and optimize the trade-off between peak reduction and forecasting accuracy. We evaluate the effectiveness of our proposed approach through the publicity Australian dataset. Results demonstrate that using our approach, the Mean average percentage error can significantly be reduced from 4.84% to 4.62%. Our work contributes to advancing the state-of-the-art in load forecasting and provides valuable insights for utilities seeking to optimize peak demand management strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".