Improved fast short-term wind power prediction model based on superposition of predicted error
Bibliographic record
Abstract
Accurate prediction of short-term wind power is an effective way to rationally adjust the scheduling strategies and to improve the operation stability and economy of microgrid with wind turbines. A new improved prediction method which does not rely on any basic prediction methods is proposed based on analysis of a traditional wind power prediction procedure and in terms of strategy how to use a basic prediction method in wind power prediction procedure. By the proposed method, an additional error prediction model is built to predict the error of the predicted results by the traditional prediction method. And the predicted error value is added back to the predicted results mentioned above to reach the final predicted results. Taking Back propagation (BP) neural network as a basic prediction method, the proposed prediction method is validated by output power prediction of a real wind farm. Support vector machine (SVM) is chosen as another basic prediction method to test the versatility of the proposed improved prediction method. The simulation results show that the proposed wind power prediction method can improve the prediction accuracy by about eight percent. The proposed method does not involve the internal characteristics of any basic prediction methods or require any auxiliary methods, which is quite different from the traditional improved methods available and thus is more universal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".