From C.elegans to Liquid Neural Networks: A Robust Wind Power Multi-Time Scale Prediction Framework
Bibliographic record
Abstract
AI, especially deep learning algorithm, has proved its potential in wind power prediction; however, the lack of explainability is the main concern to address and this work is the first to investigate the emerging Liquid Neural Network (LNN) to provide necessary transparency in wind power prediction. LNN utilizes the mathematical abstraction of C.elegans and demonstrates liquid/robust behavior in learning and estimation for unseen data. For comparative analysis, the LNN family (i.e., closed form continuous (CfC), Liquid Time Constant) and state-of-the-art recurrent networks (e.g., LSTM and GRU) and 1D-CNN are considered, and the CfC neural network provides the best results on unseen data. CfC models with fully connected layers using only 25 neurons have provided superior results for wind power prediction in different time spans, resolutions, and number of variables.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".