Emission Mitigation Dispatch in Wind Power Generation with Expedited Machine Learning
Bibliographic record
Abstract
In this paper: a machine learning (ML) module is presented for emission mitigation dispatch (EMD) in wind power (WP) generation. It is developed for the mobile edge computing (MEC) platforms. Therefore, the module is based on a lightweight ML scheme: radial basis neural network (RBN). It has been well known that RBN is compact compared with a more popular ML scheme: feedforward neural network. However, this is the first study to combine EMD with RBN, strongly motivated by the MEC implementations. MEC is becoming an important platform in the evolving smart grid program. As a trial, a case study with 36 generators and 50 wind turbines is presented. Two standard ML stages, training and testing, are conducted in the simulation. It is shown that, by using the trained RBN, the time of solving EMD is significantly reduced. It is expected to be a desirable model for MEC.
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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 | 0.001 |
| 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".