A reinforcement learning-based ensemble forecasting framework for renewable energy forecasting
Bibliographic record
Abstract
The randomness and intermittency of wind and photovoltaic power generation can negatively affect the stability of power systems . Therefore, accurate forecasting of these energy outputs is crucial for effective power system management . Among various forecasting methods, ensemble forecasting has gained attention for its superior performance and reliability. However, traditional ensemble methods, such as weighted averaging and stacking, use fixed model combinations that fail to adapt to varying wind and radiation conditions, thereby limiting their accuracy. To overcome this limitation, this study proposes a novel ensemble forecasting framework based on reinforcement learning. The framework uses a deep Q-network to dynamically select the appropriate base model for different wind and radiation conditions. The learning process is supported by a model control module, a basic forecasting module, and a performance evaluation module. Independent experiments conducted across 14 regions in China validate the framework's effectiveness, showing a significant improvement in forecasting accuracy. The framework achieved an average improvement of 12.18 % in mean absolute scaled error over base models and 4.84 % over other ensemble methods. Additionally, this study analyzes the impact of different reinforcement learning models and sample sizes on the framework's performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".