Overview
Hybrid physical-neural model for photovoltaic yield forecasting with irradiance and soiling factors.
solarpvyield
Confidence
93%
Blended from documentation depth, benchmark presence, and cross-source corroboration.
Significance score: 89/100
Source metadata
- Primary source
- GitHub
- Last updated
- 2026-05-02
- Domain
- Time Series Forecasting
Maturity assessment
Mature
Assessment combines release cadence, maintainer responsiveness, and sector-specific adoption proxies.
AI-generated summary
Hybrid physical-neural model for photovoltaic yield forecasting with irradiance and soiling factors.
Utility relevance reasoning
Supports solar portfolio forecasting and green energy reporting for sustainability programs.
Related technologies
- GridForecastNet
Time Series Forecasting
- LoadSense AI
Time Series Forecasting
- DemandSense Transformer
Time Series Forecasting
- RenewableTwin AI
Generative AI
Lineage references
Lineage Map- Upstream paper references linked in model card.
- Dataset manifests registered for reproducibility.
Source citations
- SolarYield Predictor README — installation, data expectations, and evaluation metrics.
- Model documentation — hyperparameters and reproducibility notes.
- Mobiloitte internal rubric MOBILOITTE-AIR-UR-02 — utility relevance scoring dimensions.
Timeline of changes
- 2026-05-02Maturity reassessed from Emerging → Growth based on adoption signals.
- 2026-04-18Utility relevance mapping refreshed for smart grid sector weighting.