Interpretable generalized Gaussian mixture modeling for risk-aware solar power forecasting
Bibliographic record
Abstract
Reliable photovoltaic (PV) power forecasting requires not only accurate point predictions but also transparent and well-calibrated representations of uncertainty. This paper introduces an interpretable probabilistic forecasting framework that leverages a Generalized Gaussian Mixture Model (GGMM) parameterized by a Feedforward Neural Network (FNN) to capture the non-Gaussian and heteroscedastic nature of solar power generation. The contribution lies in using the GGMM mixture parameters (mean, scale, and shape) as intrinsic indicators of forecast confidence and distributional behavior. The model is trained end-to-end using a negative log-likelihood objective and tuned through Bayesian optimization. Experiments on the GEFCom2014 and UNISOLAR datasets show that this GGMM-based formulation provides better calibrated uncertainty estimates and improved robustness under variable weather conditions compared to several strong deterministic and probabilistic baselines. In addition, analyzing the temporal evolution of the mixture weights and shape parameters offers clear interpretability by revealing regime changes, tail behavior, and confidence levels across the forecast horizon. These properties highlight the proposed framework as a practical, explainable, and decision-oriented tool for solar power forecasting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".