Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate wind speed forecasting is essential for the reliability and security of the power system, and optimal operation and management of wind integrated smart grids. However, it is still a challenging task due to the highly uncertain and volatile nature of wind speed. Accordingly, in this work, a novel deep learning-based model integrating the discrete wavelet packet transform (DWPT) and bidirectional long short-term memory (BLSTM) is developed to precisely capture deep temporal features and learn the time-varying relationship of wind speed time series. In the proposed method, by applying the DWPT, both approximations and details parts are decomposed by passing through the filters to choose the frequency band related to the features of the original signal more adaptively. The BLSTM networks are incorporated to deal with the uncertainties more effectively as they have bidirectional memory capability (feedforward and feedback loops) to investigate both previous and future hidden layers data. To simultaneously improve the forecasting performance and decrease the learning complexity, the reconstructed state space of historical wind data is employed to reflect the evolution laws of wind speed. Two case studies using real-world wind speed datasets gathered from Flatirons campus (M2) of National Renewable Energy Laboratory (NREL) located in Colorado, USA and weather station of Edmonton, Canada are implemented to demonstrate the effectiveness and superiority of the proposed hybrid method compared to the shallow architectures and state-of-the-art deep learning models in the recent literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it