Forecasting of Solar Energy Generation via Dynamic Model Ensemble
Bibliographic record
Abstract
Solar energy is one of the primary renewable energy sources. The usage of solar energy generation can help reduce carbon emissions and thus help address the challenge of global climate change. In the power system, we would like to maintain the balance between power generation and power usage. How-ever, solar energy generation is quite intermittent and will be affected by many uncontrollable factors, such as temperature, cloud cover, and humidity levels. Hence, the accurate forecasting of solar energy generation is of significant importance for the secure operation of power grids. Different types of methods have been developed for solar power generation forecasting, including statistical methods and machine learning methods. In this work, we propose a hybrid dynamic ensemble framework for solar energy generation forecasting. Specifically, a set of base forecasting learners will be first learned. Then, a set of weights will be dynamically updated for the base learners based on the expectation of the contributions of the base learners. Extensive experiment results on the real-world dataset have been implemented and demonstrated the effectiveness and robustness of our proposed method. The proposed forecasting method consistently outperforms all single-model baselines and the static ensemble model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".