A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction
Bibliographic record
Abstract
A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model.
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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) | 0.000 | 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 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".