Multi step ahead forecasting of wind power by genetic algorithm based neural networks
Bibliographic record
Abstract
In present day scenario statistical (time series) and physical (NWP) models are utilized for wind power forecasting and many of them are using neural networks to obtain greater accuracy of wind power prediction at final stage. In a time series framework, forecasting is categorised into two ways single step ahead and multi-step ahead. In this paper an advanced time-series model for multi-step ahead wind power prediction based on artificial intelligence techniques is presented. This method requires an input of past measurements for prediction & input is settled on the basis of statistical tool called Auto Correlation Function (ACF). Genetic Algorithms based Neural Network (GANN) and Feed Forward Neural Network (FFNN) trained by Levenberg-Marquardt (LM) training algorithm are employed. Mean absolute error (MAE) and mean absolute percentage error (MAPE) are considered as the performance metric and both models are also compared with persistence model. The data of wind power has been collected from Ontario Electricity Market for the year 2009-12 and tested for one year up to 12 multi-steps ahead forecasting. It has been observed that GANN gives better performance as compared to FFNN.
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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".