Solar power is rapidly becoming a major source of electricity, but its performance still swings dramatically with the weather. In Lithuania, where solar plants are multiplying, these minute-to-minute variations are increasingly difficult for grid operators to manage. A brief cloud can cut a solar module’s output by tens to hundreds of watts within seconds, turning the sky into an unpredictable control signal for the energy system.
The core challenge is timing. Electricity grids must constantly balance generation with demand, yet solar plants deliver intermittent power. When large numbers of modules are connected, sudden drops and rebounds can strain forecasting and complicate decisions about storage and reserve capacity. Even short warnings can matter when electricity must be scheduled precisely.
A research team at Kaunas University of Technology (KTU), led by Professor Rytis Maskeliūnas, introduces ShadowSense, an AI-driven approach designed to anticipate output changes caused by cloud shadows. Instead of relying on image datasets painstakingly labeled by humans, the system learns directly from the relationship between what the sky looks like and what the solar panel actually produces.
ShadowSense observes the sky using a wide-angle camera while simultaneously recording solar module power. Each abrupt decline in power becomes a “hint” that links specific cloud or shadow patterns to measurable electrical effects. Over time, the model builds a self-supervised mapping from dynamic shadow conditions to near-term power behavior.
To cope with real-world variability, the method adapts to local environments. Every solar installation has its own panel angle, surroundings, and weather characteristics, meaning a single universal model may fail. ShadowSense estimates the sun’s position, analyzes cloud motion from sky imagery, and computes how shadows are likely to land on the panel surface.
The study was validated outside the lab. An edge-ready experimental setup was installed at a residential site in Kaunas, where the camera captured sky sequences and the courtyard solar module powered the measurement and AI computing system. Over 92 days, more than 122,000 synchronized observations were collected—each combining sky frames with corresponding power data.
Results indicate that ShadowSense predicts short-term solar output changes more accurately than conventional approaches. The system reduced average forecasting error by nearly a third and detected over 92% of sudden power shifts linked to cloud shadows. For grid management, the aim is not only to know that output will fall, but to anticipate when the drop will occur.
Efficiency is also a key advantage. A single prediction required roughly 66 milliseconds and used about 0.52 joules, enabling real-time operation on low-power hardware. That makes the technology promising for distributed solar sites, remote deployments, and locations without powerful servers or stable internet links.
ShadowSense: Edge Optimized Self-Supervised Learning for Dynamic Shadow Mapping of Solar Panels is published in IEEE Transactions on Sustainable Energy. The work suggests a future where solar plants “learn their surroundings” in real time—improving reliability as renewable generation grows and variability becomes a defining feature of modern grids.
Subject of Research: Solar power forecasting under cloud-shadow variability; edge AI for self-supervised dynamic shadow mapping
Article Title: ShadowSense: Edge Optimized Self-Supervised Learning for Dynamic Shadow Mapping of Solar Panels
News Publication Date: 29-Jun-2026
Web References: https://ieeexplore.ieee.org/document/11585725
References: DOI: 10.1109/TSTE.2026.3707931; Journal: IEEE Transactions on Sustainable Energy (29-Jun-2026)
Image Credits: KTU
Keywords
Solar power forecasting; cloud shadows; edge AI; self-supervised learning; dynamic shadow mapping; smart grids; real-time prediction; renewable integration; energy storage planning
Tags: AI solar power forecastingAI-based cloud shadow detectioncloud data analysis for energy predictioncloud-based solar energy production modelsintegrating AI in solar power grid operationsKaunas University of Technology renewable energy researchMachine Learning in Renewable Energyreal-time solar energy output predictionShadowSense AI system for solar forecastingsmart grid management with AIsolar power variability managementweather impact on solar power generation
