An Optimized Long Short-Term Memory-Based Model for One Hour Ahead Wind Speed Prediction
Bibliographic record
Abstract
To achieve reliable grid operations and efficient wind energy management, accurate wind speed predictions are essential. This paper introduces a three-layer Long Short-Term Memory (LSTM) network-based wind speed forecasting model. Multiple optimizers are evaluated to determine the most effective model, with a callback function integrated to improve the training efficiency and mitigate overfitting. The model is constructed using six meteorological parameters (temperature, dew-point temperature, relative humidity, station pressure, turbulence intensity, and Richardson Number) serving as input values for wind speed prediction. The experimental evaluation of error indices, such as the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), and the $\mathbf{R}^{\mathbf{2}}$ score, shows that the RMSprop optimizer performs relatively better than the other optimizers. The proposed model demonstrates superior performance for wind speed forecasting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".