A Novel Two-Dimensional Convolutional Neural Network-Based an Hour-Ahead Wind Speed Prediction Method
Bibliographic record
Abstract
With increasing penetration of wind power, accurate prediction of wind speed is essential for planning and operation of power grids. In this paper, a novel two-dimensional (2D) convolutional neural network (CNN)-based wind speed forecasting technique is proposed for an hour-ahead wind speed prediction. The wind speed at a specific time can be predicted in less than a few milliseconds using the proposed approach and meteorological data from a few hours earlier. The input feature selection, data preprocessing, and model evaluation of the proposed approach are presented; the efficiency of 2D CNN is compared to that of one-dimensional (1D) CNN, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). A three-year historical wind speed dataset from 2020 to 2022 collected at Saskatoon International Airport in Saskatoon, Saskatchewan, Canada, is used in this study. It is found that 2D CNN shows superior performance in addressing regression and prediction challenges. Experimental results verify that the proposed 2D CNN-based forecasting techniques can provide accurate wind speed prediction. Using deep learning for wind speed prediction can reduce costs while boost energy output and contribute to sustainable and green energy development in Saskatchewan and beyond.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".