Forecasting Solar Energy Using a Single Image

2026-04-23

AI Summary

Traditional 3D modeling approaches struggle to accurately capture the impact of nearby small-scale urban structures on photovoltaic (PV) panel irradiance, resulting in low prediction accuracy and high soft costs. This work proposes a novel method that requires only a single panel-view image, integrating computer vision, geometric reasoning, and radiative physics to infer camera orientation and visible sky regions, thereby predicting direct solar, diffuse sky, and building-reflected irradiance at any given time. By incorporating a temporal smoothness assumption to model reflected components, the approach achieves, for the first time, accurate time-varying irradiance prediction for PV panels in urban canyons without explicit 3D reconstruction, while also enabling automated recommendation of optimal fixed tilt angles. Real-world experiments demonstrate superior prediction accuracy over conventional transposition models and 3D simulations, and the accompanying Solaris device efficiently captures panel-view images across diverse urban environments.

Technology Category

• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Solar panels are increasingly deployed in cities on rooftops, walls, and urban infrastructure. Although the panel costs have fallen in recent years, the soft costs of installing them have not. These soft costs include assessing the illumination (irradiance) of a panel, which is typically performed using a 3D model that fails to capture small nearby structures that impact the irradiance. Our approach uses a single image taken at the panel's location to forecast its irradiance at any time in the future. We use visual cues in the image to find the camera's orientation and the portion of the sky visible to the panel in order to forecast the irradiance due to the sun and the sky. In addition, we show that the irradiance due to reflections from nearby buildings varies smoothly over time and can be forecasted from the image. This approach enables assessing the solar energy potential of any surface and forecasting the temporal variation of a panel's irradiance. We validate our approach using real irradiance measurements in urban canyons. We show that our approach often yields more accurate irradiance forecasts compared to conventional irradiance-based transposition methods and 3D model-based simulations. We also show that a single spherical image can be used to find the best fixed orientation of a panel. Finally, we present Solaris, a device to capture the image seen by a panel in a variety of urban settings.

Problem

• solar irradiance forecasting
• urban solar energy
• solar panel placement
• sky visibility
• reflected irradiance

Innovation

• single-image irradiance forecasting
• urban solar energy assessment
• sky visibility estimation
• reflection-aware irradiance modeling
• Solaris imaging device