A Novel Combination of Mycielski-Markov, Regime Switching and Jump Diusion Models for Solar Energy
Bibliographic record
Abstract
With renewable energy sources growing, solar power generation is becoming ever more popular around the world, so forecasting and scenario analysis of Solar Photovoltaic production is benecial for grid operators and investors. In this paper, we introduce a novel combination of a Mycielski-Markov model, standard regime switching, and jump diusion models to generate 1-minute Global Horizontal Irradiance time series over any time scale. It can simulate dierent scenarios of solar irradiance in the future after being trained on empirical data. We verify our model using statistical tests to compare our simulations with those from an observed time-series in Mauritius. The resulting model is able to generate simulations retaining the statistical properties of the data. Further, we nd the proposed calibration process to be robust, and identied that splitting the day into 16 periods to be perfect balance to counter overtting. The proposed model has the potential to better understand the eects of including large scale Solar Photovoltaic generation into an energy network, value future investments, or even allow for a cost-benet analysis of subsidies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".