Solar forecasting,
made explainable.
HelioQR is a research-origin solar intelligence platform transforming deep-learning solar radiation forecasts into decisions people can understand and act on.
From forecast output
to operational insight.
Solar forecasts are useful only when teams can interpret what changed, why it matters, and what to do next. HelioQR combines a research-backed forecasting pipeline with an AI analysis layer designed for solar-energy workflows.
- Solar radiation forecasting from time-series inputs
- Anomaly and deviation interpretation
- Plain-language summaries for technical and non-technical users
- Research-to-production workflow for renewable-energy analytics
Generate a solar outlook.
Choose conditions to preview the product workflow. Forecast values below are clearly labeled prototype estimates; when the Claude API is connected, the analysis panel is generated by Claude.
Adjust the inputs and generate an outlook.
Prototype demonstration — not intended for operational energy trading or safety-critical decisions.
Built from original work,
not a wrapper-first idea.
A novel QR code-based CNN approach for solar radiation forecasting
HelioQR is an early-stage effort to translate this academic work into a usable solar-intelligence product. The commercial prototype is being developed independently from the research publication.
Renewable systems produce more data than many teams can turn into timely decisions.
Our goal is to bridge forecasting research and everyday energy operations: rigorous models underneath, clear reasoning on top.