Irradiance Prediction Service for Energy Modelling
Overview
This service provides businesses, energy providers, and public sector entities with highly accurate, location-specific maps of predicted solar irradiance, thermal irradiance, and shadowing. Designed to accelerate AI adoption in the solar energy and urban planning sectors, the service translates complex meteorological and geospatial data into ready-to-use, actionable insights. The service is built for reliable, production-grade performance. Efficient inference is achieved by exporting Physics-Informed AI models in ONNX format and executing them via ONNX Runtime on the provided infrastructure. The models process georeferenced Digital Surface Models (DSM), auxiliary parameters (longitude and latitude), and historical/current meteorological measurements (such as global and diffuse irradiances from ERA5). The AI model inference workflow is optimized to handle these complex input streams swiftly, outputting standardized geospatial maps without requiring the user to manage the underlying compute.
Outcome
End-users receive high-resolution, georeferenced maps detailing predicted solar irradiance, thermal irradiance, and surface shadowing tailored to their specific locations. These outputs directly drive business value by maximizing return on investment through optimized photovoltaic placement, reducing the costs of traditional renewable energy feasibility studies, and mitigating risks in complex infrastructure planning.
Service Card
| Category | AI products |
| Domain | Solar Energy, Urban Planning, Geospatial Analytics |
| Vertical | Green Transition |
| Target users | Research Institutions, SMEs, Public Sector |
| Service type | AI Models |
| Outputs | High-resolution, georeferenced maps of predicted solar irradiance, thermal irradiance, and surface shadowing. |
| Requirements | |
| Limitations | |
| Availability | Q1/2027 |
| Licensing | |
| Visual content | assets/service-visuals/irradiance.png |
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