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PreprintCC BY 4.0Zenodo

SOLAR-CORE: HELIOS — Grey-Box Parametric Photovoltaic Digital Twin & Physics-Informed Neural Network (PINN) for n-TOPCon Yield, Grid Stability, and Environmental Impact Assessment (Ley 21/2013)

  • Kelvin Jesus Flores Yarihuaman1
  1. 1Yggdrasil Environmental Systems
Open at sourcePDFDOICite

Abstract

Utility-scale and distributed photovoltaic deployments across the Iberian Peninsula face a dual bottleneck: complex multi-jurisdictional environmental permitting under Spain's Ley 21/2013 and EU Directive 2023/2413 (RED III), and electro-thermal performance uncertainty under high summer irradiance, weak distribution nodes (REE P.O. 12.2), and aeronautical servitudes (AESA Real Decreto 384/2023).

SOLAR-CORE: HELIOS V1 (Open-Core Release) resolves this fragmentation through a unified Level-2 Parametric Photovoltaic Digital Twin (ISO 23247) that couples satellite-assimilated anisotropic sky transposition (EC JRC PVGIS-SARAH2, Perez 1990 & 5-component HDKR) and n-TOPCon cell thermodynamics (Faiman 2008, IEC 61215 / IEC TS 62804-1) with preventive territorial buffer verification (Red Natura 2000, Public Hydraulic Domain RDL 1/2001, Livestock Trails Ley 3/1995), Sandia SGHAT retinal glare optics, Pasquill-Gifford PM10 fugitive dust modeling (RD 102/2011), and 25-year Project Finance DCF bankability analysis.

At its computational core, HELIOS executes a 11.41M-parameter Spatio-Temporal Transformer Physics-Informed Neural Network (HeliosSpatialTemporalTransformerPINN, R&sup2; = 0.9912, MAE T_cell = 0.35 °C, latency < 1 ms) governed in real time by the PhysicsSentinelSidecar and encapsulated by the Athena & Astraea Double DNA Armor (109 verified statutory standards and 12 seminal scientific papers).

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Cite

BibTeXRIS
BibTeX
@misc{yarihuaman2026solarcore,
  title = {{SOLAR-CORE: HELIOS — Grey-Box Parametric Photovoltaic Digital Twin & Physics-Informed Neural Network (PINN) for n-TOPCon Yield, Grid Stability, and Environmental Impact Assessment (Ley 21/2013)}},
  author = {Kelvin Jesus Flores Yarihuaman},
  howpublished = {Zenodo},
  year = {2026},
  month = oct,
  doi = {10.5281/zenodo.23506041},
  url = {https://zenodo.org/records/23506041},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - SOLAR-CORE: HELIOS — Grey-Box Parametric Photovoltaic Digital Twin & Physics-Informed Neural Network (PINN) for n-TOPCon Yield, Grid Stability, and Environmental Impact Assessment (Ley 21/2013)
AU  - Kelvin Jesus Flores Yarihuaman
PY  - 2026
DA  - 2026/10/20
PB  - Zenodo
DO  - 10.5281/zenodo.23506041
UR  - https://zenodo.org/records/23506041
AB  - Utility-scale and distributed photovoltaic deployments across the Iberian Peninsula face a dual bottleneck: complex multi-jurisdictional environmental permitting under Spain's Ley 21/2013 and EU Directive 2023/2413 (RED III), and electro-thermal performance uncertainty under high summer irradiance, weak distribution nodes (REE P.O. 12.2), and aeronautical servitudes (AESA Real Decreto 384/2023).  SOLAR-CORE: HELIOS V1 (Open-Core Release) resolves this fragmentation through a unified Level-2 Parametric Photovoltaic Digital Twin (ISO 23247) that couples satellite-assimilated anisotropic sky transposition (EC JRC PVGIS-SARAH2, Perez 1990 & 5-component HDKR) and n-TOPCon cell thermodynamics (Faiman 2008, IEC 61215 / IEC TS 62804-1) with preventive territorial buffer verification (Red Natura 2000, Public Hydraulic Domain RDL 1/2001, Livestock Trails Ley 3/1995), Sandia SGHAT retinal glare optics, Pasquill-Gifford PM10 fugitive dust modeling (RD 102/2011), and 25-year Project Finance DCF bankability analysis.  At its computational core, HELIOS executes a 11.41M-parameter Spatio-Temporal Transformer Physics-Informed Neural Network (HeliosSpatialTemporalTransformerPINN, R&sup2; = 0.9912, MAE T_cell = 0.35 °C, latency < 1 ms) governed in real time by the PhysicsSentinelSidecar and encapsulated by the Athena & Astraea Double DNA Armor (109 verified statutory standards and 12 seminal scientific papers).
ER  -