Wind Speed Prediction for a Target Station Using Neural Networks and Particle Swarm Optimization
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
In this study, artificial neural networks (ANN) and particle swarm optimization (PSO) were applied to predict the average daily wind speed measured at a meteorological tower installed June 2005 at the Greater Moncton Sewerage Commission, in Moncton, New Brunswick, Canada. Wind speeds were collected covering the period between June 2005 and December 2008. Five reference airport meteorological stations were used as input for the neural network and PSO. The artificial neural network modeling was done using the Matlab® neural network toolbox, while the PSO algorithm is an in-house program written in Matlab®. The daily wind speeds generated by the ANN model and PSO were compared with the actual measured data. It was found that with six months of input data, both the ANN and the PSO were able to predict the short term daily wind speed for the following 36 months at the target station. The PSO obtained a smaller error compared to the neural network. The PSO algorithm was also able to find the best combination of input variables automatically, while the ANN used manually-selected input variables.
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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