Stochastic Model for Generating Synthetic Hourly Global Horizontal Solar Radiation Data Sets Based on Auto Regression Characterization
Bibliographic record
Abstract
A large number of non-repetitive multi-year hourly solar radiation time-series dataset are desired when applying Monte Carlo techniques for planning and design of solar energy systems. Solar radiation models which utilize the clearness index and average value decomposition methods are commonly used to generate synthetic set of solar irradiances for this purpose. In this paper, a novel stochastic solar radiation model based on probability distributions of the first-order differences of hourly global solar horizontal radiation is proposed. The first-order differences are modeled using a trend component and a stochastic component represented using the cumulative distribution functions, both extracted from historical data taken over a window of 31 days around the considered day of the year. Measured solar radiation data from four different locations with varying climate characteristics were used to evaluate the proposed model in comparison to two previously reported models. The proposed method performed consistently better in terms of the similarity of probability distributions and autocorrelation functions, for all four locations and datasets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".