Data Augmentation-Based Photovoltaic Power Prediction
Bibliographic record
Abstract
In recent years, as the grid-connected installed capacity of photovoltaic (PV) power generation has increased by leaps and bounds, it has assumed considerable importance in predicting PV power output. However, the power prediction of newly built small-scale PV power plants often suffers from many unexpected problems, such as data redundancy, data noise, data sample imbalance, or even missing key data. Motivated by the above facts, this paper proposes a data augmentation-based prediction framework for PV power. Firstly, the daily power distance measurement is used to analyze feature correlation and filter erroneous data. Then, the autoencoder network trained based on the random discarding mechanism is used to restore the PV power generation data. At the same time, a specific data augmentation method for the PV power curve is designed to eliminate the influence of data sample imbalance. In the final experimental section, compared with the latest method, this method achieved the highest MAE accuracy of 8.26% and RMSE accuracy of 10.96%, which proves the effectiveness of this method.
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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 | 0.001 | 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".