Gaussian Mixture Model for the Estimation of Multiyear Solar Irradiance Probability Density
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Bibliographic record
Abstract
The increasing popularity of photovoltaic resources and the connection of solar farms in larger sizes to power distribution networks make it imperative for network designers to assess the variability of available solar resources at a given location. This is normally achieved by attempting to obtain an accurate estimation of the probability density function (pdf) of solar irradiance at the given site. The parametric beta distribution has long been a popular choice in such studies because of its ease of use. However, pdf estimation using parametric functions can lead to inaccurate models and suboptimal decisions being made about the suitability of potential farm site. In this article, a more robust estimation of solar irradiance pdf than that given by the popular beta distribution is obtained by using a Gaussian mixture model (GMM). Using multiyear solar data, the GMM estimate is also compared with a widely used nonparametric kernel density estimation model that employs a common rule-of-thumb bandwidth selection method. Assessments are carried out using a goodness-of-fit test, three error measures, and the coefficient of determination index. Results demonstrate the improved accuracy and robustness of the GMM, which consistently achieves better performance metrics compared with the kernel density estimation (KDE) model and the beta distribution.
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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