Computational Acquisition of Meteorological Data for Applications in Electric Power Systems
Bibliographic record
Abstract
Humans heavily rely on mother nature for hospitable living conditions, plentiful harvests, and energy generation, yet have no control. The best humans can do is plan and predict. Climatological statistics and forecasts provided by public weather services serve as traditional methods for obtaining meteorological information. However, through Numerical Weather Prediction models, one can simulate climate fluctuations with high spatial resolution over long periods. Uses for Numerical Weather Prediction models include analyzing the energy flux of smart homes, smart grid technology, impact on power transmission infrastructure, and energy production through wind and photovoltaic farms. The efficiency of these technologies is dependant on the surrounding weather phenomena. The optimization of these systems to the environment upon which they exist can both reduce wasted resources and the economic impact on consumers and organizations. This paper outlines the methods used for the acquisition of weather data through computer simulations at a spatial resolution of 1.2 km in 15-minute intervals with an accuracy of 2%.
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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 | 0.001 | 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".