Bayesian Optimization Based ANN Model for Short Term Wind Speed Forecasting in Newfoundland, Canada
Bibliographic record
Abstract
Wind power capacity around the world is increasing day by day, but the production of wind energy greatly depends on the wind speed, where the wind speed has stochastic nature over time. In this paper, an artificial neural network (ANN) technique to forecast wind speed for the next hour in Newfoundland, Canada is proposed. As, deep learning models are combined with different hyperparameters, in our study, the selection of important hyperparameters are conducted by applying the Bayesian optimization algorithm. The wind speed forecasting performance of the proposed model is compared with other recognized models like support vector machine (SVM), random forest (R.F.) and decision tree (D.T.), where it is observed that our proposed model performs better than the other models in terms of mean absolute error (M.A.E.) and root mean squared error (R.M.S.E.). The proposed Bayesian optimized artificial neural network is fed with five input features and delivers M.A.E. and R.M.S.E. of 1.09 and 1.45.
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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".