Optimized Hybrid Neural Network for Wind Speed Forecasting
Bibliographic record
Abstract
Though wind power capacity all over the world is increasing rapidly, the availability of wind power generation mostly reclines on wind speed, which is a random variable with stochastic nature. Therefore, robust technique with powerful feature extraction capability is required to predict wind speed accurately. In this paper, we have recommended a hybrid model using convolutional neural network (CNN) and long-short term memory (LSTM). where CNN is used for extracting fuzzy input features and LSTM to catch the sequence to predict wind speed accurately. As deep learning models are associated with multiple hyper-parameters with great impact, Bayesian optimization algorithm is used for hyper-parameter tuning. Additionally, the performance of some established machine learning models are added on the same data-set. It is observed that, the proposed Bayesian optimized CNN-LSTM hybrid model surpasses the other four established models like SVM, ANN, CNN and LSTM in terms of different performance evaluation metrics like mean absolute error, root mean error and root mean square error.
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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) | 0.001 | 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 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".