AI/ML Solar Nowcasting Based on Computer Vision
End-to-end solar nowcasting embedded pipeline within the Girasol Machine, based on computer vision to fuse multi-sensor data for very short-term solar radiation forecasting
This project develops an end-to-end AI/ML framework for solar nowcasting (very short-term forecasting) using a real-time stream of infrared sky images from the Girasol system, enabling continuous extraction of cloud features and dynamic updating of intra-hour forecasts.
End-to-end ML/AI pipeline for solar nowcasting using real-time infrared sky imaging and multi-sensor data fusion.
The embedded architecture comprises three learning modules.
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Module 1 (Physics-informed learning):Estimates and visualizes wind velocity fields from image streams, enabling prediction of cloud motion and Sun-occlusion events (Terrén-Serrano & Martínez-Ramón, 2021). -
Module 2 (Bayesian learning):Detects clouds and associates each cloud to an estimated wind flow using mixture models in a Hidden Markov Chain (Terrén-Serrano & Martínez-Ramón, 2023). -
Module 3 (Probabilistic deep learning):Fuses multi-sensor data—including sky images, weather station measurements, and pyranometer data to generate probabilistic forecasts of solar irradiance over 1–15 minute horizons (Terrén-Serrano & Martínez-Ramón, 2023).
Forecasting event (bright green), very short-term probabilistic functional forecast (green), partially observed solar radiation (black), ground-truth unobserved radiation (red), persistence forecast (gray), and deterministic functional forecast orange.
Together, these modules form a unified computational framework for accurate, real-time solar forecasting to support reliable power system operations under uncertainty.
This work is part of my Ph.D. dissertation at the University of New Mexico.