Deep Spatial-Temporal 2-D CNN-BLSTM Model for Ultrashort-Term LiDAR-Assisted Wind Turbine's Power and Fatigue Load Forecasting
Bibliographic record
Abstract
Optimizing wind turbine performance is still a challenge due to the dynamic interactions between the spatially temporally stochastic wind fields and the wind turbine as a complex mechanical system. Recent cost reduction of remote sensing wind measurement technologies, such as light detection and ranging (LiDAR), has opened a new research area on the use of deep learning models for predicting wind turbine's responses. In this article, a LiDAR-aided deep learning model is presented to learn the powerful spatial-temporal characteristics from the input wind fields. In the proposed method, the combination of 2-D convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) units is used to capture high levels of abstractions in wind fields concurrently, and thus, forecasting wind output power and fatigue load as two representatives of wind turbine responses. The LiDAR wind preview information is used as the 2-D-images of wind fields for the CNN. Moreover, the BLSTM is incorporated with the proposed CNN to improve the forecasting accuracy further and learn deep temporal features. The aero-elastic 5-MW reference wind turbine of National Renewable Energy Laboratory (NREL) is used to evaluate the performance of proposed model compared to the state-of-the-art deep-learning-based architectures in the recent literature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".